New Techniques in Oncologic Imaging

New Techniques in Oncologic Imaging

New Techniques in Oncologic Imaging edited by

Anwar R. Padhani, F.R.C.P., F.R.C.R. Mount Vernon Cancer Centre London, U.K.

Peter L. Choyke, M.D. Molecular Imaging Program National Cancer Institute Bethesda, Maryland, U.S.A.

Published in 2006 by CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2006 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-10: 0-8493-9274-8 (Hardcover) International Standard Book Number-13: 978-0-8493-9274-0 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. No part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC) 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Catalog record is available from the Library of Congress

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To Our mentors—Janet Husband and Andrew Dwyer Our wives—Femeeda and Lynda Our children—Shahid, Sabiha, Aliya and Adam, Sarah For their patience and continued support

Foreword

As the wealth of laboratory and clinical research in cancer takes us beyond today’s practice into the new paradigm for 21st century medicine in which malignant disease will be detected much earlier, where tumors will be genotypically profiled and factors influential in phenotype will become known, and where treatments will be targeted according to abnormalities in specific molecular pathways and directed to eradication of small volume disease using highly sophisticated surgical and radiotherapy techniques, so medical imaging will take center stage. Thus we are at the dawn of a new era in medicine and the vision of imaging in this new era has been captured by Drs. Padhani and Choyke in their book New Techniques in Oncologic Imaging. The goal of cancer imaging is to provide a detailed portrait of a tumor by combining exquisite morphological information with pathophysiological and metabolic measurements. The elegant anatomical detail provided by three-dimensional, multi-slice computed tomography (CT) is likely to continue as the mainstay for providing morphological information on the presence and extent of tumor, tumor volume, and anatomic relationships. Morphological information provided by magnetic resonance imaging (MRI) surpasses CT in well-defined anatomical sites and in the evaluation of specific tumors, so the role of MRI in this context continues to evolve. However, the fundamental requirement remains—the need to provide detailed morphological information about relevant biological processes including angiogenesis, hypoxia, apoptosis, cellular membrane integrity, necrosis, and other malignancy specific processes. Based on these parameters, clinicians will be able to make more precise management decisions noninvasively, thereby fine-tuning cancer therapy whether it be with targeted drugs, surgery, or radiotherapy. Such developments in imaging will also allow new therapies to be developed such as robotic surgical procedures and focused physical therapies. This text is aimed at those who are or wish to be engaged in advanced imaging including scientists, physicists, chemists, biologists; radiologists, and clinicians—all of whom have an increasing need for the latest information on advances in cancer imaging techniques that will underpin much of the future developments in clinical practice. Drs. Padhani and Choyke have recognized and responded to this exciting challenge in New Techniques in Oncologic Imaging. A major advantage of this text is that the editors have brought together state-of-the-art evidence in all the different imaging modalities being applied to cancer research and, in so doing, have provided a comprehensive review of the whole field of morphological and functional measurement of tumors. This text will not only allow those focused on one aspect of research v

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to acquaint themselves with progress in other modalities, but will set the scene for functional imaging to be viewed as a whole rather than as a topic of measurement confined to one modality. At this time of staggering advances in imaging, this approach provides an outstanding contribution to medical literature. Surely this will guide and inform future clinical practice, which will be based on integrated morphology, pathophysiological, and metabolic information rather than being confined to measurement provided by a single technique. Drs. Padhani and Choyke are both outstanding experts in cancer imaging who have contributed individually to the growing body of literature in the field of functional imaging. They have brought their expertise and experience together in New Techniques in Oncologic Imaging, and have recruited a superb team of experts from many different fields of cancer imaging from the United States and Europe. The text covers ultrasound, CT, magnetic resonance, nuclear medicine, and positron emission tomography, and looks to the future with reviews on electron spin resonance, optical imaging, and bioluminescence. The important issue of image processing, central to the development and effective use of modern imaging, is also appropriately considered. The editors are to be congratulated for their vision and commitment in bringing this exciting project to fruition. Janet E. Husband, OBE. FMedSci, FRCP, FRCR Professor of Diagnostic Radiology Royal Marsden NHS Foundation Trust President, Royal College of Radiologists London, U.K.

Foreword

The nexus between oncology and imaging grows continually stronger. Today, imaging is a key enabling biomarker for modern oncologic practice. The traditional requirement of oncologists was to stage disease; i.e., to define the extent of involvement by cancer. With structurally oriented imaging, it is now possible to obtain images from head to toe within minutes by computed tomography (CT) and magnetic resonance imaging (MRI). We today have imaging technologies that enable assessment at multiple levels of resolution from the whole body to a targeted molecular pathway. Moreover, these powerful imaging methods can now be harnessed to guide the delivery of therapeutic interventions such as minimal invasive surgery, high precision radiotherapy, or percutaneous ablations. Modern multi-slice spiral CT and fast gradient MRI are now routinely available in most cancer centers and both physicians and patients have high expectations that these methodologies will continue to rapidly improve and to deliver tangible clinical benefits. Despite their continued and rapid improvement in resolution, structural imaging modalities like X-ray, CT, and static MRI rely on morphology that is fundamentally non-specific. The past decade has now seen the addition of functional or physiologic information on top of excellent anatomic depiction. The best example of this is fluorodeoxyglucose (FDG) imaging, which has revolutionized the practice of oncology. While its clinical development and acceptance has taken more than ten years, it is now possible and accepted to classify lesions according to their level of glucose metabolism. In some cases, this directly relates to treatment response and prognosis. Additionally, dynamic contrast enhanced MRI (DCE-MRI) and MR spectroscopy (MRS) have provided novel insights into the behavior of disease at diagnosis and during treatment. Functional imaging represents a major step forward in the evolution of oncologic imaging and it will rapidly continue to evolve with targeted molecular and nano-compound enabled imaging. Novel imaging methodologies, currently being developed seek to identify neoplasms at the limits of their detectability. Methods such as optical imaging and radionuclide and PET imaging are capable of detecting biochemical compounds at the nano to pico-molar concentration level. This sensitivity is matched only by the specificity with which such targeted imaging probes are able to seek out and label cancers. The promise of the non-invasive ‘‘virtual biopsy,’’ in which all of the metabolic, proteomic, and genomic information needed to make management decisions can be derived from imaging, is not as far fetched today as it was ten years ago. Indeed, reporter gene strategies have proved the principle that highly specific imaging probes can selectively enhance targeted tissue. vii

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Ultimately, it is hoped that these new imaging methods will aid in the detection of pre-malignant conditions, early detection of cancer, early monitoring of therapeutic interventions, and in discovering recurrence before it causes irreparable damage. As a complement to drug therapy, imaging biomarkers truly have the potential that they may soon help select patients for particular therapies, identify proper dosing, and aid in drug discovery processes. The future of imaging in oncology is exciting and filled with technologic and biologic wonders. New Techniques in Oncologic Imaging captures this spirit of innovation and wonder. Written and edited by experts from around the world representing major cancer centers, it fills a void by providing an up-to-date resource for oncologists and imagers. One hopes that it will stimulate the reader to higher levels of understanding of the capabilities of advanced imaging, and will motivate further exploration of the promising possibilities and capabilities in oncology. Imaging has come a long way from the original discovery of X-rays by Wilhelm Ro¨ntgen at the end of the 19th century. New Techniques in Oncologic Imaging represents an excellent resource to update one’s knowledge of the uninterrupted progress in oncologic imaging over the last 100 years. Michael V. Knopp, M.D., Ph.D. Professor and Chairman Department of Radiology Ohio State University Columbus, Ohio, U.S.A.

Preface

Twenty years ago diagnostic radiology departments were far simpler places. The cancer patient could expect to undergo a series of radiographic studies or fluoroscopy, perhaps an ultrasound or simple axial computed tomography all of which were recorded on film. Now, cancer patients undergo any number of highly specialized computed tomography or MRI scans, positron emission tomography, radionuclide, or sonographic studies in order to characterize their disease and for staging and treatment planning purposes. Traditionally, each examination was evaluated and the next recommended on the basis of the results obtained. Today, speed seems to be of the essence and multiple examinations are often quickly performed in succession ‘‘according to protocols.’’ A computed tomographic scan that may have taken 30 minutes in the past now takes only seconds to complete and can be reformatted into non-orthogonal planes without loss of resolution or even used to create threedimensional models. Moreover, today’s patient may be offered a bewildering array of minimally invasive diagnostic and therapeutic interventional procedures. All of the images obtained will be recorded and displayed electronically. The increased sophistication of oncologic imaging demands increased understanding on the part of health care providers and consumers about the strengths and limitations of the choices available. This book helps readers gain that understanding. We have gathered experts from Europe and the United States to discuss their areas of expertise in a readable format and to quickly convey the theory and technology underlying their particular speciality. Just as yesterday’s technology was considered optimal in its time (but soon became outdated), so today’s technology is just a step away from obsolescence. Newer technologies will surely replace older ones at an increasing pace. Already, there are indications of major shifts in the direction of oncologic imaging. Previously imaging focused on improving spatial resolution and data acquisition speed to achieve excellence in anatomic resolution that we take for granted today, there is an increasing recognition that imaging must provide more than just sharp pictures. Increasing importance is now placed on ‘‘functional imaging information’’; that is, imaging that depicts critical physiologic processes within tumors, such as angiogenesis or metabolism, which can then be spatially matched with anatomic images. Thus, we see the development of clinically relevant ‘‘functional’’ imaging such as position emission tomography (PET), dynamic contrast enhanced MRI, and MR spectroscopy fused to ‘‘conventional imaging’’ like computed tomography and MRI. Looking further ahead, the future clearly points to molecularly targeted imaging agents that bind specifically to particular tumors or reporter gene systems, where ix

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transfected cells ‘‘report’’ the status of therapeutic genes by inserting imaging ‘‘beacons’’ or enzymes that entrap exogenously delivered imaging ‘‘beacons’’ intracellularly. The prospect of such systems in the clinical environment is enticing and is encouraging the development of research programs around the world. Much of the current enthusiasm is prompted by the expected interplay between imaging and therapy in the future. Molecular imaging, the name that has been used to describe imaging that reflects biologic or molecular processes in vivo, will be an important adjunct to targeted molecular therapies. As we move toward personalized medicine where cancer patients are treated not by the anatomical site of the tumor but by its phenotype or molecular signature, so it is hoped that molecular imaging will guide patient selection and will tailor therapy to the individual patient’s needs. Once such therapies are begun, these same imaging techniques will be used to determine whether drug delivery to the target system or cell has occurred, whether specific molecular pathways are suitably modulated, and whether underlying pathophysiological processes are being altered—all before anatomic improvements are visible. If treatments are successful, then patients will still need to be monitored for recurrent disease that may be detected before morphological alternations have taken place. Thus, molecular imaging will be highly integrated with molecular therapies. At this point in its development, an understanding of the basic precepts of molecular imaging will help prepare the reader for the coming revolution in molecular medicine. This book is composed of chapters written by experts in their respective fields from Europe and the United States. Each chapter provides an overview of the current status of the modality or technology but cannot be comprehensive in scope. A wealth of information is available on each topic and guidance is supplied in the references. It is hoped that this effort will whet the appetites of interested readers for more knowledge. We are indebted to the many contributors to this book and the helpfulness of our Editor, Geoffrey Greenwood and his editorial and production teams, in seeing this project to its completion. Peter L. Choyke Anwar R. Padhani

Contents

Foreword Foreword Preface . . . Contributors

Janet E. Husband . . . . v Michael V. Knopp . . . . vii . ix . . . . xvii

PART I. ADVANCES IN MATURE IMAGING TECHNOLOGIES 1. Advances in Computed Tomography . . . . . . . . . . . . . . . . . . . . . . . 1 Rendon C. Nelson Principles of Multidetector CT . . . . 3 The Future of CT . . . . 17 References . . . . 18 2. Advances in MRI of the Brain . . . . . . . . . . . . . . . . . . . . . . . . . . 21 John Butman, Myles Koby, and Peter L. Choyke Introduction . . . . 22 Hardware Advances . . . . 22 Software Advances . . . . 25 3D Spoiled Gradient Echo . . . . 25 Image Management . . . . 33 Challenges in Neuroradiology . . . . 33 Conclusions . . . . 34 References . . . . 34 3. Advances in Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 David Cosgrove, Chris Harvey, Martin Blomley, and Rob Eckersley Introduction . . . . 38 Imaging and Doppler . . . . 38 References . . . . 62 4. MR Lymphangiography: Technique . . . . . . . . . . . . . . . . . . . . . . . 65 Anuradha Saokar, Mukesh Harisinghani, and Jelle Barentsz Introduction . . . . 66 xi

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Composition and Mechanism of Action of Ferumoxtran-10 . . . . 66 Timing of Contrast Enhanced Imaging . . . . 66 MR Imaging . . . . 66 Conclusions . . . . 75 Appendix . . . . 75 References . . . . 76 5. Image Processing in Tumor Imaging . . . . . . . . . . . . . . . . . . . . . . 79 Jianhua Yao Image Segmentation . . . . 80 Image Measurement and Quantification . . . . 85 Image Display and Visualization . . . . 87 Image Registration . . . . 92 Computer-Aided Diagnosis/Detection . . . . 98 Discussion and Conclusion . . . . 99 References . . . . 100 6. Advances in Radiotherapy Planning . . . . . . . . . . . . . . . . . . . . . . 103 Cynthia Me´nard, Deborah Citrin, Kevin Camphausen, James Deye, Norman C. Coleman, and Robert C. Susil Introduction . . . . 104 External Beam Photon Therapy (Teletherapy) . . . . 104 ‘‘Stereotactic’’ Forms of Teletherapy . . . . 111 Brachytherapy . . . . 112 Simulation and Delineating the Radiation Target . . . . 114 Biological Imaging and the Future of Radiation Oncology . . . . 119 Conclusion . . . . 120 References . . . . 120

PART II. NEW IMAGING TECHNOLOGIES 7. Clinical PET in Oncology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Gary J. R. Cook and Wai Lup Wong Principles of PET . . . . 126 Diagnosing Cancer . . . . 130 Staging Cancers . . . . 132 Prognostication . . . . 136 Cancer Recurrence . . . . 136 Assessment of Residual Disease . . . . 138 Cost Effectiveness of 18FDG PET . . . . 140 Conclusions . . . . 140 References . . . . 140

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8. Clinical Advances in PET and Tracer Development . . . . . . . . . . . 145 Ludwig G. Strauss and Antonia Dimitrakopoulou-Strauss Data Acquisition, Image Reconstruction, and Evaluation . . . . 146 PET Correlates with Molecular Biology . . . . 151 Other Tracers . . . . 155 Other New Tracers . . . . 162 References . . . . 163 9. Molecular Targeted Imaging in Oncology with Radioscintigraphy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 David J. Yang and E. Edmund Kim Introduction . . . . 168 Production of 99mTc-EC–Agent Conjugate . . . . 170 Conclusion . . . . 187 References . . . . 190 10. Magnetic Resonance Spectroscopy in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Jeffry R. Alger Introduction . . . . 194 Technical Background . . . . 194 Technical Limitations . . . . 198 Cancer Biochemistry Relevant to MRS . . . . 200 Clinical Uses of MRS . . . . 204 Future of MRS in Oncology . . . . 207 References . . . . 208 11. Dynamic MRI Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Anwar R. Padhani and David J. Collins Introduction . . . . 214 Imaging Tissue Vascularity with MR Imaging . . . . 214 T2-Weighted DSC-MRI . . . . 216 T1-Weighted DCE-MRI . . . . 223 DCE-MRI vs. f-MDCT . . . . 231 Challenges for Perfusion DCE-MRI . . . . 232 Conclusions . . . . 235 References . . . . 236 12. Functional Computed Tomography . . . . . . . . . . . . . . . . . . . . . . 245 Ken Miles Introduction . . . . 246 Functional CT and Tumor Physiology . . . . 246 Physiological Models . . . . 247 Technical Validation and Reproducibility . . . . 249 Clinical Validation and Applications in Oncology . . . . 249 Advantages and Limitations . . . . 252

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Future Developments . . . . 253 Summary . . . . 254 References . . . . 254 13. BOLD Imaging of Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Simon P. Robinson Introduction . . . . 258 Intrinsic-Susceptibility Contrast MRI . . . . 258 Physiological Origins of the Tumor BOLD MRI Response to Carbogen . . . . 260 Intrinsic-Susceptibility Contrast MRI and Tumor Oxygenation . . . . 262 Intrinsic-Susceptibility Contrast MRI and Tumor Angiogenesis . . . . 264 Intrinsic-Susceptibility Contrast MRI and Tumor Response to Antivascular Therapy . . . . 266 Limitations and Future of Intrinsic-Susceptibility Contrast MRI . . . . 267 Summary . . . . 269 References . . . . 269 14. Pharmacokinetic Modeling of Dynamic Contrast Enhanced MRI in Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Peter L. Choyke, David Thomassen, and Andrew J. Dwyer General Kinetic Model . . . . 275 Conclusion . . . . 287 Appendix: Derivation of the GKM . . . . 288 References . . . . 289 PART III. PROMISING ‘‘NEW BREED’’ TECHNOLOGIES 15. Bioluminescence Reporter Gene Imaging in Small Animal Models of Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Tarik F. Massoud and Sanjiv S. Gambhir Introduction . . . . 292 Techniques and Instrumentation . . . . 296 Sensitivity of Bioluminescence Imaging in Living Subjects . . . . 297 Principles of Reporter Gene Imaging . . . . 299 Applications of Bioluminescence Reporter Gene Imaging in Cancer Models . . . . 301 Multiplex and Multimodality Bioluminescence Imaging in Living Mice . . . . 311 Combined In Vitro, Ex Vivo, and Living Subject Bioluminescence Imaging . . . . 312 Future Outlook . . . . 313 References . . . . 315

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16. Diffusion MR Imaging in Tumors . . . . . . . . . . . . . . . . . . . . . . . 319 Andrzej Dzik-Jurasz and Simon Doran Introduction . . . . 320 Physical Principles and Technical Considerations . . . . 320 Diagnosis, Grading, and Physiological Significance of DW-MRI in Cancer . . . . 329 Prediction and Assessment of the Response to Anticancer Treatment . . . . 331 DW-MRI in Treatment Planning . . . . 333 Conclusions . . . . 335 References . . . . 335 17. Noninvasive Determination of Tissue Oxygen Concentration by Overhauser Enhanced Magnetic Resonance Imaging . . . . . . . . 341 Sean J. English, Koen Reijnders, Kenichi Yamada, Nallathamby Devasahayam, John A. Cook, James B. Mitchell, Sankaran Subramanian, and Murali C. Krishna Introduction . . . . 342 Overhauser Enhanced MRI: Combination of MRI and EPRI . . . . 343 Mapping pO2 with OMRI . . . . 347 Limitations and Future Directions . . . . 348 Summary . . . . 349 References . . . . 349 18. Advances in Optical Imaging of Cancer . . . . . . . . . . . . . . . . . . . 351 Alexander M. Gorbach, Vasilis Ntziachristos, and Lev T. Perelman Optical Imaging Based on Internal Contrasts to Identify Cancer . . . . 352 Optical Imaging Based on External Contrasts to Identify Cancer . . . . 363 Summary . . . . 367 References . . . . 368 Index . . . . 371

Contributors

Jeffry R. Alger Department of Radiological Sciences, Ahmanson-Lovelace Brain Mapping Center, Brain Research Institute, Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, U.S.A. Jelle Barentsz Department of Radiology, Radboud University Medical Center Nijimegen, Nijimegen, The Netherlands Martin Blomley Imaging Sciences Department, Imperial College, Hammersmith Hospital, London, U.K. John Butman Diagnostic Radiology Department, Warren G. Magnusson Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A. Kevin Camphausen Radiation Oncology Branch, CCR, NCI, NIH, DHHS, Bethesda, Maryland, U.S.A. Peter L. Choyke Molecular Imaging Program, National Cancer Institute, Bethesda, Maryland, U.S.A. Deborah Citrin Radiation Oncology Branch, CCR, NCI, NIH, DHHS, Bethesda, Maryland, U.S.A. Norman C. Coleman Radiation Oncology Sciences Program, NCI, NIH, DHHS, Bethesda, Maryland, U.S.A. David J. Collins Cancer Research UK Clinical Magnetic Resonance Research Group, Institute of Cancer Research and The Royal Marsden NHS Trust, Sutton, Surrey, U.K. Gary J. R. Cook Department of Nuclear Medicine, Royal Marsden Hospital, Sutton, Surrey, U.K. John A. Cook Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. David Cosgrove Imaging Sciences Department, Imperial College, Hammersmith Hospital, London, U.K. xvii

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Nallathamby Devasahayam Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. James Deye Radiation Oncology Sciences Program, NCI, NIH, DHHS, Bethesda, Maryland, U.S.A. Antonia Dimitrakopoulou-Strauss Medical PET Group—Biological Imaging, Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany Simon Doran

University of Surrey, Guildford, Surrey, U.K.

Andrew J. Dwyer Diagnostic Radiology Department, National Institutes of Health, Bethesda, Maryland, U.S.A. Andrzej Dzik-Jurasz CR UK Clinical Magnetic Resonance Research Group, Institute of Cancer Research and Royal Marsden NHS Trust, Sutton, U.K. Rob Eckersley Imaging Sciences Department, Imperial College, Hammersmith Hospital, London, U.K. Sean J. English Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. Sanjiv S. Gambhir The Crump Institute for Molecular Imaging, Departments of Molecular and Medical Pharmacology and Biomathematics, and UCLA-Johnsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, U.S.A.; and Department of Radiology and the Bio-X Program, Stanford University School of Medicine, Stanford, California, U.S.A. Alexander M. Gorbach Department of Diagnostic Radiology, National Institutes of Health, Bethesda, Maryland, U.S.A. Mukesh Harisinghani Division of Abdominal Imaging and Interventional Radiology, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A. Chris Harvey Imaging Sciences Department, Imperial College, Hammersmith Hospital, London, U.K. E. Edmund Kim Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A. Myles Koby Diagnostic Radiology Department, Warren G. Magnusson Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A. Murali C. Krishna Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A.

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Tarik F. Massoud The Crump Institute for Molecular Imaging, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, U.S.A.; Departments of Radiology and Oncology, University of Cambridge School of Clinical Medicine, Cambridge, U.K. Cynthia Me´nard Radiation Oncology Branch, CCR, NCI, NIH, DHHS, Bethesda, Maryland, U.S.A. Ken Miles Brighton and Sussex Medical School, Falmer, Brighton, U.K. James B. Mitchell Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. Rendon C. Nelson U.S.A.

Duke University Medical Center, Durham, North Carolina,

Vasilis Ntziachristos Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A. Anwar R. Padhani

Mount Vernon Cancer Centre, London, U.K.

Lev T. Perelman Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, U.S.A. Koen Reijnders Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. Simon P. Robinson Division of Basic Medical Sciences, St. George’s, University of London, London, U.K. Anuradha Saokar Division of Abdominal Imaging and Interventional Radiology, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A. Ludwig G. Strauss Medical PET Group—Biological Imaging, Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany Sankaran Subramanian Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. Robert C. Susil Department of Biomedical Engineering, Johns Hopkins University Schoool of Medicine, Baltimore, Maryland, U.S.A. David Thomassen Diagnostic Radiology Department, National Institutes of Health, Bethesda, Maryland, U.S.A. Wai Lup Wong Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, Middlesex, U.K.

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Kenichi Yamada Radiation Biology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, U.S.A. David J. Yang Division of Diagnostic Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A. Jianhua Yao Department of Diagnostic Radiology, Warren G. Magnusson Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A.

PART I. ADVANCES IN MATURE IMAGING TECHNOLOGIES

1 Advances in Computed Tomography Rendon C. Nelson Duke University Medical Center, Durham, North Carolina, U.S.A.

A Note from the Editors

C

omputed tomography (CT) is the mainstay of cancer imaging outside of the central nervous system. Advances in multidetector CT (MDCT) technology have had a profound impact on its diagnostic capabilities. Such techniques as multiphase, single breath-hold imaging, CT angiography (CTA), volume rendering and virtual colonography owe their success to the development of multidetector arrays with continuously moving gantries. However, to achieve these advantages without incurring the penalty of increased radiation exposure, care must be exercised in selecting the optimum combination of slice thickness, X-ray beam collimation, and table speed.

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Part I. Advances in Mature Imaging Technologies

When discussing computed tomography (CT), it is interesting and useful to review the evolution of this technology since its inception. In the early 1970s, axial scanners were first introduced. These scanners had large cables attaching the X-ray tube to the power supply (1–3). This limitation resulted in intermittent X-ray exposures (eventually as short as two seconds) and incremental table motion. With these scanners, it was difficult to obtain more than about 10 slices per minute and slices were typically on the order of 10 mm in thickness. As a result, imaging of the chest and abdomen, for example, was primarily performed during the longer and more sustaining venous and/or equilibrium phases. Furthermore, since the chest and upper abdomen was scanned during multiple breath-holds, respiratory misregistration was a significant problem. In the early 1990s, helical scanners were introduced, having a slip-ring connection between the X-ray tube and the power supply (4–10). This allowed for both continuous tube rotation and continuous table motion. As a result, a volumetric dataset was acquired consisting of up to 75 slices per minute with a slice thickness that was typically about 5 mm. Owing to the speed of these scanners, dynamic multiphasic imaging was introduced which was particularly advantageous in organs such as the liver and pancreas, where imaging during both the arterial and venous phases improved both the detection and the characterization of various tumors (Fig. 1) (11– 19). Because an entire organ, such as the liver and pancreas, could be scanned during a single breath-hold, there was also a marked reduction in respiratory misregistration artifacts. Furthermore, noninvasive CT–angiography (CTA) became a reality, although in the initial stages anatomic coverage was somewhat limited because of the relatively slow table speeds (20–24). In the early- to mid-1990s, dual slice helical CT scanners were introduced which allowed for either twice the anatomic coverage or half the acquisition time. There was otherwise little change in CT parameters and, as a result, the quality of the datasets was relatively similar to single slice helical scanners. In the late 1990s, four-slice helical scanners were introduced, resulting in a precipitous increase in table speeds (25). With these scanners, for example, one could image the entire abdomen and pelvis (approximately

Figure 1 Biphasic liver imaging: (A) Axial image through the upper abdomen during the late hepatic arterial phase reveals a hyper-enhancing mass in the caudate lobe (segment I) (arrow). The mass is relatively homogenous and there is no evidence of either a central scar or a capsule. (B) During the portal venous phase, the mass demonstrates subtle hypo-attenuation and is much less conspicuous. This is a metastasis from an endocrine primary tumor.

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400 mm of longitudinal coverage) during a single breath-hold with slices as thin as 2.50 mm (26,27). When less anatomic coverage was needed, slices as thin as 1.25 mm could be acquired. This resulted in a dramatic improvement in both the temporal resolution for multiphasic examinations and in the spatial resolution for CTA examinations. As certain regions of the body such as the entire abdomen and pelvis could be imaged during a single breath-hold, there were essentially no misregistration artifacts. The trade-off, however, was an increase in the radiation dose compared to single detector CT scanners. In the early 2000s, 8- and 16-slice multidetector helical scanners were introduced (28). Compared to four-slice scanners, these scanners had the following imaging options: (i) scan much faster while obtaining a helical dataset of similar image quality, (ii) scan slightly faster while obtaining a dataset of slightly better image quality, and (iii) scan during a similar time while obtaining a much better helical dataset. Overall, the result was much higher quality three-dimensional (3D) and multiplanar reformations, which could be obtained in virtually every patient. Furthermore, on the sixteen-slice scanners the voxels can be nearly isotropic, meaning that the dimensions in the x-, y-, and z-axes are similar. Radiation doses could actually be reduced slightly on the 8 and 16 slice compared to four-slice multidetector scanners, although they continue to be higher than single detector scanners. PRINCIPLES OF MULTIDETECTOR CT Differences Between Single Detector and Multidetector CT On a single detector CT (SDCT) scanner, the detector consists of a single slab of ceramic material and the slice thickness is simply determined by the collimator (Fig. 2). In comparison, on a multidetector CT (MDCT) scanner the detector has a matrix array, which consists of a ceramic detector divided into small individual pieces separated by thin metallic septae (29,30). With these scanners, the slice thickness is determined not only by the collimator but also by the detector configuration (Fig. 3). In addition, multidetector CT scanners have X-ray tubes with a much higher heat-loading capacity and have shorter gantry rotation times (e.g.,

Figure 2 Diagram of a single detector helical CT scanner: It is noted that the detector consists of a single slab of ceramic material (e.g., total of 20 mm in the z-axis) and that the slice thickness is determined by the collimation width.

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Figure 3 Diagram of a MDCT (multidetector computed tomography) scanner. It is noted that the detector consists of a row of small individual squares of ceramic material (e.g., four slice scanner consisting of 16 detectors each measuring 1.25 mm in the z-axis for a combined footprint of 20 mm) and that the slice thickness is determined both by the collimation width and the detector configuration.

0.75–1.0 seconds for SDCT vs. 0.4–0.8 seconds for MDCT). These short gantry rotation times are in part facilitated by shorter tube-to-isocenter distances, which effectively reduce rotational inertia (31). An additional advantage of a shorter tube-to-isocenter distance is the fact that there is less X-ray flux thereby requiring lower mAs. For example, reducing the distance from 630 to 541 mm increases the X-ray flux by 36% [(630/541)2 ¼ 1.36], thereby allowing for a 74% (1/1.36 ¼ 0.74) reduction in the mA (32). One of the interesting features of MDCT scanners is the phenomenon of focal spot wobble (32). This focal spot motion causes the X-ray beam to move back-andforth on the detector so that the collimators must be opened to irradiate the specified detectors consistently within the matrix (Fig. 4). As a result, focal spot wobble can cause a substantial increase in radiation dose. Currently, all of the manufactures provide hardware and software solutions to this problem.

Figure 4 Focal spot wobble. Because of rotational factors the focal spot moves back-andforth on the anode (white arrow). This causes the X-ray beam to move back-and-forth on the detector, as well. To prevent partial radiation of key detectors (black arrow) the collimators are opened, thus further increasing the radiation dose.

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In general, radiation doses are significantly higher on MDCT scanners compared to SDCT scanners (32). First, the X-ray beam is collimated to a much wider width. For example, on a SDCT scanner the beam collimation to achieve a 5-mm thick slice X-ray width is 5 mm. On a MDCT scanner, the beam collimation for a 5-mm thick slice width can vary from 10 to 20 mm. To minimize this effect, the combination of a narrower collimation and a higher pitch is preferred over the combination of a wider collimation and a slower pitch. For example, comparing the 4
5.00 mm detector configuration at a pitch of 0.75 to that of a 4
2.50 mm detector configuration at a pitch of 1.5, one diminishes the radiation dose by about 25% without changing the table speed (15 mm per gantry rotation) or acquisition time (33). Second, is the effect of the penumbra. The X-ray beam has two components, (i) the umbra that is the central and most usable portion of the X-ray beam and (ii) the penumbra, which is the peripheral and unusable portion of the beam (Fig. 5). On the four-slice MDCT scanner, the penumbra represents a significant percentage of the beam; therefore, the collimation width must be increased to irradiate specified detectors with the umbra. Fortunately, on 8- and 16-slice scanners, the penumbra, represents a much smaller percentage of the total beam and therefore, has a lesser effect. The thin septations in a matrix detector are in the order of 90 to 100 mm in thickness and represent dead space since X-ray photons striking these metallic septae do not contribute to the image. This results in a higher radiation dose. Furthermore, scanners with 8- and 16-slice capability have more septations resulting in more dead space and a further increase in radiation dose. Fortunately, these scanners can partially counteract this effect by using higher pitches and as a result, faster table speeds.

Detector Configurations An example of the various detector configurations on a four-slice MDCT scanner manufactured by General Electric (Milwaukee, Wisconsin, U.S.A.) is as follows (26): The matrix array on this particular scanner consists of sixteen 1.25-mm thick

Figure 5 The X-ray beam consists of the umbra, the constant and usable portion of the beam, and the penumbra, the tapering and unusable portion of the beam. As the width of the penumbra is relatively the same, it has less of an impact with wide compared to narrow collimation as well as with 8 or 16 slice compared to 4-slice MDCT scanners.

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Figure 6 Depiction of the 4
1.25 mm detector configuration. It is noted that the outer 12 detectors do not contribute to image reconstruction. With this configuration, 1.25, 2.50, 3.75, and 5.00-mm thick slices can be reconstructed. This configuration is associated with the slowest table speeds, the fewest artifacts, and the highest quality dataset. Owing to limited anatomic coverage, it is mainly used for single organ CT angiography.

detectors oriented in the z-axis and having a maximum footprint of 20 mm. With the 4
1.25 mm detector configuration, the four central detectors are irradiated and slice thicknesses of 1.25, 2.50, 3.75, or 5.00 mm can be reconstructed (Fig. 6). The maximum table speed using a pitch of 1.5 with this configuration is 7.5 mm per gantry rotation (up to 15 mm per second using a 0.5-second gantry rotation time). As a result, even though this yields the best spatial resolution in the z-axis, slower table speeds limit anatomic coverage. In the abdomen, for example, this is an acceptable tradeoff for certain organ-specific applications such as in the liver, kidneys, or pancreas. With the 4
2.50 mm detector configuration, the central eight detectors are irradiated and slice thicknesses of 2.50, 3.75, and 5.00 mm can be reconstructed (Fig. 7). It is noted that a slice thickness of 1.25 mm is not available with this configuration. The maximum table speed using a pitch of 1.5 is 15 mm per gantry rotation (30 mm per

Figure 7 Depiction of the 4
2.50 mm detector configuration. It is noted that the outer eight detectors do not contribute to image reconstruction. With this configuration, 2.50-, 3.75-, and 5.00-mm thick slices can be reconstructed. A slice thickness of 1.25 mm is not available. Due to an excellent combination of slice thickness and table speed, this configuration is widely used for CT angiography.

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Figure 8 Depiction of the 4
3.75 mm detector configuration. It is noted that the outer four detectors do not contribute to image reconstruction. With this configuration, 3.75 and 5.00-mm thick slices can be reconstructed. Slice thicknesses of 1.25 and 2.50 mm are not available. This configuration is widely used in pediatric patients. It is not a good choice for CT angiography.

second using a gantry rotation time of 0.5 seconds). As a result, this configuration and pitch is an excellent compromise between slice thickness in the z-axis and anatomic coverage, and for example, is widely used for routine imaging of the abdomen and pelvis (single breath-hold in 12 to 20 seconds) as well as CTA. With CTA, the images are reconstructed with a thickness of 2.50 mm at a 1.00 mm interval (60% overlap) (34,35). With the 4
3.75 mm detector configuration, the central 12 detectors are irradiated, yielding slice thicknesses of 3.75, 5.00, and 7.50 mm (Fig. 8). It is to be noted that at the higher pitch of 1.5 the minimum slice thickness is 5.00 mm because of the slice broadening in the z-axis and that slice thicknesses of 1.25 and 2.50 mm are not available, regardless of the pitch. This configuration is primarily used for pediatric imaging since a slice thickness of 3.75 mm is popular in these smaller individuals. Furthermore, it is used with a higher pitch (e.g., 1.5) when faster table speeds are required during the fleeting hepatic arterial phase of liver imaging (22.5 mm per rotation or up to 45 mm per second using a 0.5 second gantry rotation time). Although there tends to be more streak artifacts at the faster table speeds, they are acceptable at 22.5 mm per rotation. With the 4
5.00 mm detector configuration, all 16 detectors are irradiated yielding slice thicknesses of 5.00 and 7.50 mm (Fig. 9). It is to be noted that a slice thickness of 1.25, 2.50, or 3.75 mm is not available at this configuration. This particular configuration is used infrequently since at the lower pitch radiation doses are inordinately high (compared to the 4
2.50 detector configuration at a pitch of 1.5) and at the higher pitch, images are significantly degraded by streak artifacts (because of aliasing).

Definitions of Pitch There are two definitions of pitch, (i) the slice pitch and (ii) the beam pitch. The slice pitch was the original but less meaningful definition, which is defined as: Table travel (per gantry rotation)/Thickness of one of multiple slices:

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Figure 9 Depiction of the 4
5.00 mm detector configuration. It is noted that all 16 detectors contribute to image reconstruction. With this configuration, only 5.00-mm thick slices can be reconstructed. Slice thicknesses of 1.25, 2.50, and 3.75 mm are not available. This configuration is associated with the fastest table speed but the most artifacts. It will not yield an adequate dataset for either 3D or multiplanar reformations.

For example, if the detector configuration is 4
2.50 mm and the table travel is 15 mm per gantry rotation, the pitch is 15/2.50 ¼ 6. The beam pitch is a more meaningful definition, which is defined as: Table travel (per gantry rotation)/Collimation width (additive thickness of all slices). For example, using a 4
2.50 mm detector configuration and a table travel of 15 mm per gantry rotation (same as above), the pitch is 15/10 ¼ 1.5. The reason ‘‘beam pitch’’ is the preferred term since a pitch of less than one is associated with an overlap or redundancy in radiating some of the detectors following two gantry rotations. This results in an inordinate increase in the radiation dose. For example, with a pitch of 0.75 there is redundancy or oversampling of 25% of the slices (one slice on a 4-slice scanner, 2 slices on an 8-slice scanner, and 4 slices on a 16-slice scanner) (Fig. 10). With a pitch of 1.5, there is no overlap between the first and second gantry rotations and in fact, there is a 50% gap (Fig. 11). However, this does not imply that there is a gap in data acquisition but it does mean that the radiation dose is lower. The trade-off is that with higher pitches the number of photons per voxel is diminished resulting in more noise. An exception for using a pitch less than one is in applications such as the staging of pancreatic adenocarcinoma, when high spatial resolution is needed and radiation dose is less of an issue.

Formula for Determining the Detector Configuration Knowledge of what detector configuration is being used for a specific application is helpful, not only for understanding the table and the acquisition speed but also for determining what slice thicknesses are available for retrospective reconstruction. To calculate the detector configuration, one must determine two parameters, (i) the pitch and (ii) the table speed. Then, by simply dividing the table speed by the pitch (preferably the beam pitch), the collimation width can be determined. For example,

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Figure 10 Beam pitch: example of a beam pitch of 0.75. It is noted that there is a 25% overlap (1 slice in a 4-slice scanner, 2 slices in an 8-slice scanner, and 4 slices in a 16-slice scanner) following two gantry rotations. This overlap improves the quality of the dataset but increased the radiation dose.

using a pitch of 1.5 and a table speed of 15 mm per gantry rotation, the collimation width is 15/1.5 ¼ 10 mm. The detector configuration using this example would be 4
2.50, 8
1.25, and 16
0.625 mm on 4-slice, 8-slice, and 16-slice scanners, respectively. It is possible to reconstruct slices thicker but not thinner than acquired (minimum thickness of 2.50, 1.25, and 0.625 mm, respectively, in the above example).

Figure 11 Beam pitch: example of a beam pitch of 1.50. It is noted that there is a 50% separation rather than an overlap following two gantry rotations. This lack of overlap diminishes the quality of the dataset but decreased the radiation dose.

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Figure 12 Coronal reformations of the upper abdomen. (A) There is a well-circumscribed low attenuation mass in the lower pole of the right kidney suggesting a simple cyst (arrow). It is noted that the image is quite noisy since it has been reconstructed with a thickness of 1 mm in the z-axis (antero–posterior direction). (B) When the same coronal image is reconstructed with a thickness of 5 mm in the z-axis, there is a significant decrease in noise. The trade-off, however, is an increase in partial volume average, which can make lesion characterization difficult. This is especially problematic for small lesions.

Principles of Reconstructing Images Thicker than Acquired The goal with MDCT is to obtain the best possible volumetric dataset by choosing a detector configuration, which couples the thinnest possible slices with a pitch that yields a table speed and an acquisition time that is compatible with the patient’s breath-holding capacity. One of the strategies to reduce image noise on the axial images is to reconstruct the slices thicker than acquired. For example, 2.50 or 5.00 mm thick images are reconstructed from images that are acquired from 4
1.25, 8
1.25, or 16
0.625 mm detector configurations (Fig. 12). This also has the added benefit of reducing the number of images, which is critical in practices that print and read from film. With these datasets, thinner slices can always be reconstructed retrospectively (as long as the raw data is available) when high quality 3D or multiplanar reformations are required or when thinner slices are needed to reduce the effects of partial volume averaging (for example, characterizing an adrenal nodule or a cystic renal mass).

Principles of Overlapping Reconstructions For CTA, it has been shown that 3D datasets are improved by overlapping slice reconstructions. In general, a 50% to 60% overlap is advantageous (34,35). For example, on a SDCT scanner, 3-mm thick slices should be reconstructed at 1 mm intervals resulting in a 67% overlap. On a MDCT scanner, 2.50-mm thick slices should be reconstructed at 1.00 mm intervals yielding a 60% overlap (Figs. 13 and 14). It has to be kept in mind that with overlapping reconstructions there is

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Figure 13 Nonoverlapping reconstruction: example of a nonoverlapping reconstruction, where 3 mm thick slices are reconstructed at 3 mm intervals. This would be acceptable for axial image review but not for either 3D or multiplanar reformations.

no change in the slice thickness, although the number of slices is dramatically increased because of the overlap.

Evolution in Voxel Sizes Toward Isotropia In making the transition from four to eight slices per gantry rotation, there is no change in the nominal voxel size. For example, using a field-of-view of 360 mm and a detector configuration of 4 or 8
1.25 mm, the voxel size is 360/512 ¼ 0.7 mm in

Figure 14 Overlapping reconstruction: example of overlapping reconstruction, where 3 mm thick slices are reconstructed at 1 mm intervals. It is noted that while there is a change in the reconstruction interval (e.g., 1 mm), there is no change in the slice thickness (e.g., 3 mm). An overlap of 60% or more is optimal for 3D or multiplanar reformations.

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Figure 15 Examples of images acquired with a 16
0.625 mm detector configuration and reconstructed at various slice thicknesses. (A) An axial images reconstructed at 0.625 mm thick has increased noise due to fewer photons per voxel. There is, however, much less partial volume averaging in the z-axis (cranio-caudal direction). (B) The same axial image reconstructed at 5.00 mm thick has much less noise but more partial volume averaging. To make off-axis reformations, the axial images are first reconstructed 0.625 mm thick at 0.50 mm interval. This dataset was then rendered in either a straight or curved format. (C) In this coronal image, the slice thickness in the z-axis (antero–posterior direction) is 0.625 mm. (D) In this coronal image, the slice thickness in the z-axis is 5.00 mm, dramatically reducing the noise while increasing partial volume averaging.

the x-axis, 0.7 mm in the y-axis, and 1.25 mm in the z-axis (plus nominal slice broadening as determined by the pitch). When making the transition from 8 to 16 slices per gantry rotation, the nominal voxel size can be smaller. For example, using a 360 mm field-of-view and a detector configuration of 16
0.625 mm, the voxel size is 360/ 512 ¼ 0.7 mm in the x-axis, 0.7 mm in the y-axis and 0.625 mm in the z-axis (plus nominal slice broadening as determined by the pitch). For the first time in the history of CT, the voxels can be isotropic, meaning that the dimensions of the voxel are

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Figure 16 Coronal reformations of the entire abdomen and pelvis acquired with an 8
1.25 mm detector configuration and pitch of 1.68. The axial images are reconstructed with a thickness of 1.25 mm at 0.50-mm intervals. A coronal image is then reformatted with a thickness of 5.00 mm in the z-axis (antero–posterior direction) to decrease noise. (A) There is a large metastasis in the right hepatic lobe. (B) No masses are noted on an image obtained more anteriorly, but there is excellent depiction of the stomach due to distention of the lumen with water and enhancement of the wall with intravenous contrast material.

equal in the x-, y-, and z-axes. Although spatial resolution in all three axes is improved, the trade-off is fewer photons per voxel and higher image noise. Since kV and mA are set to near maximum on an eight-slice MDCT scanner to achieve adequate image quality with low noise, there are few options to increase the technique further on a 16-slice scanner. As a result, until, X-ray tubes with higher heat loading capacity are available, images acquired at 16
0.625 mm will be inherently noisy, which may limit some applications, such as CT angiography (Fig. 15).

Fundamental Advantages of MDCT The first advantage of MDCT is the ability to scan a lot faster while acquiring a dataset with similar slice thickness and image quality to that of SDCT. Faster table speeds are particularly advantageous in patients with reduced breath-holding capacity since table speeds on the order of 50 to 70 mm per second are achievable. Fast table speeds are often chosen in the setting of trauma, in patients with dyspnea, in young children or in those patients in whom there are communication difficulties such as a language barrier or deafness.

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Figure 17 Volume rendered projection CT angiogram of the abdominal aorta and iliac arteries revealing a large infrarenal aortic aneurysm. It is noted that the entire dataset was acquired during a single breath-hold on the order of 15 seconds.

The second advantage of MDCT is the ability to scan faster while acquiring a dataset with a similar slice thickness but better image quality, compared to SDCT. For example, this is the strategy used for routine imaging of the abdomen and pelvis in patients with average breath holding capacity. With these parameters, the entire abdomen and pelvis (350–400 mm of longitudinal coverage) can be scanned in a single breath-hold of 12 to 20 seconds in duration (Fig. 16). It is recommended that faster gantry rotation speeds (e.g., 0.5 seconds per rotation) should be selected whenever possible. Although images acquired using shorter gantry rotation times are reconstructed using fewer trajectories in the algorithm (i.e., undersampling), image quality is not significantly degraded (31). An exception might be encountered when scanning through the shoulders and bony pelvis or in large patients where aliasing in the form of streak artifacts may be encountered.

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Figure 18 (A) Axial CT colonographic image acquired following insufflation of the entire colon with air. A moderate-sized polyp is noted in the cecum (arrow). It is noted that the internal attenuation of the polyp is similar to adjacent retroperitoneal fat, consistent with a lipoma. The lipoma is also well visualized on the coronal (B) and the sagittal (C) reformation and on a virtual endoscopic view (D).

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Figure 19 (A) An axial image through the upper abdomen reveals an approximately 2 cm hypoattenuating mass in the pancreatic head (arrow). The mass abuts the superior mesenteric vein but does not appear to invade the wall or lumen. (B) Antero–posterior volume rendered projection image reveals conventional anatomy as well as an unusual ‘‘hairpin’’ course of the proximal proper hepatic artery, without evidence of narrowing or irregularity (arrow). (C) Curved planar reformation through the main pancreatic duct reveals dilatation down to the head of the pancreas where there is a hypoattenuating mass (M). Curved planar reformations through the splenic artery (D), and superior mesenteric artery in both the coronal (E) and sagittal (F) planes reveal no evidence of tumor encasement or adjacent lymphadenopathy. (G) Curved planar reformation through the hepatic artery reveals circumferential tumor encasement at the level of the ‘‘hairpin’’ tortuosity (arrow). (Continued on next page).

The second advantage of MDCT noted above is particularly useful for multiplanar reformations and CTA (36). When large areas of anatomy need to be scanned such as the abdominal aorta and iliac arteries (Fig. 17) or in CT-colonography (Fig. 18), faster table speeds are selected to complete the acquisition during a reasonable breath-hold and/or during peak arterial enhancement (37,38). On a four-slice scanner, the parameters for such a protocol would include a 4
2.50 mm detector configuration and a pitch of 1.5. This dataset will yield relatively high-resolution images for 3D and multiplanar reformations, particularly for larger blood vessels that are perpendicular to the axial plane. The third advantage of MDCT includes the ability to scan during a similar time with either thinner slices and/or much better quality images, compared to

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Figure 19(E–G) (Continued).

SDCT. These datasets can be acquired during a comfortable breath-hold (10–20 seconds) with reasonable anatomic coverage (200–300 mm). For example, in the abdomen, this advantage is particularly applicable when evaluating single organs such as the liver, pancreas, or kidneys. As less anatomic coverage is needed to scan these organs, slower table speeds and thinner slices can be used to provide very high quality datasets for 3D and multiplanar reformations (28). Specific applications include the staging of pancreatic adenocarcinoma (Fig. 19), evaluation of the liver prior to hepatic tumor resection (Fig. 20) or transplantation, and the evaluation of the kidney prior to partial or donor nephrectomy.

THE FUTURE OF CT In the future, CT scanners that have much faster image reconstruction, higher heat-loading capacity X-ray tubes, even faster table speeds, and automatic volume rendering of the datasets may be expected. The latter feature will give the radiologist the ability to change the slice thickness, location, and orientation during interpretation, interactively. There will be ongoing efforts to reduce the radiation dose without

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Figure 20 Antero–posterior volume rendered projection image of the upper abdomen in a patient anticipating hepatic tumor resection reveals a large replaced right hepatic artery (arrow). The entire left hepatic lobe is perfused from the proper hepatic artery. If placement of a chemotherapy infusion pump was indicated in this patient, the replaced right hepatic artery would be ligated to allow collaterals from the left hepatic artery to crossover to the right hepatic lobe. A catheter would then be positioned in the gastroduodenal artery for drug infusion.

sacrificing image quality and to improve reconstruction algorithms. Improvement in the x- and y-axis dimension of the voxel may even be witnessed, which have not changed since the inception of this useful technology.

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8. Costello P, Anderson W, Blume D. Pulmonary nodule: evaluation with spiral volumetric CT. Radiology 1991; 179:875–876. 9. Costello P, Ecker CP, Tello R, Hartnell GG. Assessment of the thoracic aorta by spiral CT. Am J Roentgenol 1992; 158:1127–1130. 10. Heiken JP, Brink JA, Sagel SS. Helical CT: abdominal applications. Radiographics 1994; 14:919–924. 11. Bluemke DA, Fishman EK. Spiral CT of the liver. Am J Roentgenol 1992; 160:787–792. 12. Bonaldi VM, Bret PM, Reinhold C, Mostafa A. Helical CT of the liver: value of an early hepatic arterial phase. Radiology 1995; 197:357–363. 13. Hollett MD, Jeffrey RB Jr, Nino-Murcia M, Jorgensen MJ, Harris DP. Dual-phase helical CT of the liver: value of arterial phase scans in the detection of small malignant hepatic neoplasms. Am J Roentgenol 1995; 164:879–884. 14. Oliver JH III, Baron RL, Federle MP, et al. Detecting hepatocellular carcinoma: value of unenhanced or arterial phase CT imaging or both used in conjunction with conventional portal venous phase contrast-enhanced CT imaging. Am J Roentgenol 1996; 167:71–77. 15. Baron RL, Oliver JH III, Dodd GD III, Nalesnik M, Holbert BL, Carr B. Hepatocellular carcinoma: evaluation with biphasic, contrast-enhanced, helical CT. Radiology 1996; 199:505–511. 16. van Leeuwen MS, Noordzij J, Feldberg MA, Hennipman AH, Doornewaard H. Focal liver lesions: characterization with triphasic spiral CT. Radiology 1996; 201:327–336. 17. Hollett MD, Jorgensen MJ, Jeffrey RB. Quantitative evaluation of pancreatic enhancement during dual-phase helical CT. Radiology 1995; 195:359–361. 18. Dupuy DE, Costello P, Ecker CP. Spiral CT of the pancreas. Radiology 1992; 183: 815–818. 19. Lu DSK, Vendantham S, Krasny RM, Kadell B, Berger WL, Reber HA. Two-phase helical CT for pancreatic tumor: pancreatic versus hepatic phase enhancement of tumor, pancreas and vascular structures. Radiology 1996; 199:697–701. 20. Napel S, Marks MP, Rubin GD, et al. CT angiography with spiral CT and maximum intensity projection. Radiology 1992; 185:607–610. 21. Diedrichs CG, Keating DP, Glatting G, Oestmann JW. Blurring of vessels in spiral CT angiography: effects of collimation width, pitch, viewing plane and windowing in maximum intensity projection. J Comput Assist Tomogr 1996; 20:965–974. 22. Galanski M, Prokop M, Chavan A, Schaefer CM, Jandeleit K, Nischelsky JE. Renal arterial stenosis: spiral CT angiography. Radiology 1993; 189:185–192. 23. Rubin GD, Dake MD, Napel S, et al. Spiral CT of renal artery stenosis: comparison of three-dimensional rendering techniques. Radiology 1994; 190:181–189. 24. Raptopoulos V, Rosen MP, Kent KC, Kuestner LM, Sheiman RG, Pearlman JD. Sequential helical CT angiography of aortoiliac disease. Am J Roentgenol 1996; 166:1347–1354. 25. McCollough CH, Zink FE. Performance evaluation of a multislice CT system. Med Phys 1999; 26:2223–2230. 26. Killius JS, Nelson RC. Logistic advantages of four-section helical CT in the abdomen and pelvis. Abdom Imaging 2000; 25:643–650. 27. Cuomo FA, Brink JA. Radiation dose with multidetector row CT: comparison of high and low pitch scanning strategies for abdominal imaging [Abstract]. Radiology 2000; 81:505. 28. Gupta AK, Nelson RC, Johnson GA, Paulson EK, Delong DM, Yoshizumi TT. Optimization of eight-element multidetector row helical CT technology for evaluating the abdomen. Radiology 2003; 227:739–745. 29. Hu H, He HD, Foley WD, Fox SH. Four multidetector-row helical CT: image quality and volume coverage speed. Radiology 2000; 215:55–62. 30. Hu H. Multislice CT: scan and reconstruction. Med Phys 1999; 26:5–18.

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31. Bergin D, Heneghan JP, Ho LM, Nelson RC. Multidetector helical CT of the chest, abdomen and pelvis: does increasing the gantry rotation speed from 0.8 to 0.5 seconds affect image quality? [Abstract]. Am J Roentgenol 2002; 178:43. 32. Yoshizumi TT, Nelson RC. Radiation issues with multidetector row helical CT. Crit Rev Comput Tomogr 2003; 44:95–117. 33. General Electric Light Speed Protocol Simulator; Proprietary data. 34. Urban BA, Fishman EK, Kuhlman JE, Kawashima A, Hennessey JG, Siegelman SS. Detection of focal hepatic lesions with spiral CT: comparison of 4- and 8-mm interscan spacing. Am J Roentgenol 1993; 160:783–785. 35. Brink JA, Wang G, McFarland EG. Optimal section spacing in single-detector helical CT. Radiology 2000; 214:575–578. 36. Schoepf UJ, Becker CR, Bruening RD, et al. Multislice CT angiography. Brit Inst Radiol 2001; 13:357–365. 37. Rubin GD, Shiau MC, Leung AN, Kee ST, Logan LJ, Sofilos MC. Aorta and iliac arteries: single versus multiple detector-row helical CT angiography. Radiology 2000; 215:670–676. 38. McFarland EG, Brink JA, Pilgram TK, et al. Spiral CT colonography: reader agreement and diagnostic performance with two- and three-dimensional image-display techniques. Radiology 2001; 218:375–383.

2 Advances in MRI of the Brain John Butman and Myles Koby Diagnostic Radiology Department, Warren G. Magnusson Clinical Center, National Institutes of Health, Bethesda, Maryland, U.S.A.

Peter L. Choyke Molecular Imaging Program, National Cancer Institute, Bethesda, Maryland, U.S.A.

A Note from the Editors

P

ractically every innovation in magnetic resonance imaging (MRI) has been applied first to the brain. The brain’s well-defined anatomy, MR characteristics, and relative absence of motion make it ideal for MRI. Advances in hardware, such as higher field strengths, high performance gradients, and advanced coil designs have led to brain scans with more contrast obtained at higher speeds. Pulse sequence advances such as echo planar imaging, fluid attenuated inversion recovery, diffusion weighted, perfusion imaging, and spectroscopic images have expanded the diagnostic range of MRI. Image processing and image management have also had a profound impact on MRI of the brain. This chapter reviews these developments and provides illustrative examples.

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INTRODUCTION Magnetic resonance imaging (MRI) is the premier modality for evaluating brain tumors. The relatively small size of the brain along with the relative absence of motion makes high-resolution imaging routinely possible. Moreover, the well-known MRI behavior of white and gray matter and cerebrospinal fluid (CSF) makes them ideal for optimizing contrast parameters. The paucity of air and fat reduces the artifacts often seen with body MRI. For these reasons, MRI of the brain has been the focus of most new developments in technology. Such developments are often introduced in MRI of the brain before they are attempted in other parts of the body. These natural advantages of MRI of the brain create an excellent platform for the study of brain tumors. The advances in MRI technology of the brain can be divided into hardware, software, and image processing improvements. These advances combine to create rapid, high-resolution scans that provide unique biologic information. This chapter will review these recent advances.

HARDWARE ADVANCES Advances in magnet technology, gradient speed and strength, coil design and parallel processing, and table control have been instrumental in improving the capabilities of brain MRI. Here we consider each separately. Magnet Design Perhaps the most significant recent advance in MRI of the brain has been the development of high-field strength magnets. ‘‘High-field’’ now generally refers to field strengths of 3T or higher, since most superconducting magnets have 1.5T. Commercial high-field MR units now range from 3 to 8T, and experimental units of 12T have been designed. The principal advantage of high-field strength is the higher signal-to-noise ratio (SNR) resulting from the higher proportion of spins that align with the magnetic field as opposed to spins that align against the magnetic field (1). This gain in signal can be used in a variety of applications, including higher resolution scans, faster scans, more highly resolved NMR spectroscopy, and blood oxygen level determination (BOLD) studies (2). Excellent spatial resolution is important for detecting small lesions in the brain (e.g., metastases) and fine structures such as the cranial nerves or inner ear structures that may be affected by brain tumors (3). However, increasing spatial resolution is more demanding of signal strength; a 50% increase in resolution requires a four-fold increase in signal to maintain a constant SNR. Similarly, faster imaging speeds, are required for dynamic contrast-enhanced (DCE) studies, which are more demanding of signal strength; a 50% reduction in scan time requires a doubling of signal to maintain SNR. Enhancement may be greater at higher field strengths for the same dose of contrast (4). MR spectroscopy, discussed in detail later in this chapter and in a separate chapter, is also improved by higher field strength because individual metabolite peaks can be resolved at higher field, whereas at lower field these metabolites may be combined into a single peak (5). BOLD imaging, which is used to identify functionally critical regions of the brain that must be spared during surgical

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therapies, is also improved by the higher SNR of high-field strengths. BOLD depends on subtle shifts in the relative ratio of oxyhemoglobin to deoxyhemoglobin within tissue and the signal change is often quite small (1–3%) (2,3). Thus, changes in local blood flow occurring in response to performing a specific task are more easily and reliably detected at higher field. Time-of-flight magnetic resonance angiography (MRA), which depicts the vessels of the circle of Willis, is also improved at higher fields since the in-flow enhancement effects are greater. There are a number of potential disadvantages to higher MR fields. In addition to the added costs of the magnet and the difficulties in siting the unit due to larger fringe fields and heavier magnets, there are increased safety concerns at high-field strength. Heating deposition, which is measured in units of specific absorbed radiation (SAR), is a greater concern at higher fields. SAR depends on radiofrequency absorption and tissue attenuation which is greater at the higher resonance frequencies required by higher field strengths. Thus, pulse sequences that might be perfectly safe at 1.5 T may produce unacceptable heating due to SAR at higher field strengths. As a result, pulse sequences normally performed at 1.5 T must be altered to prevent overheating the patient. For instance, the substitution of lower SAR gradient echo images at 3 T for higher SAR T1-weighted spin echo images at 1.5 T has made interpretation of the bone marrow within the skull for possible metastatic disease more difficult. Additionally, some patients experience dizziness or nausea due to stronger and faster magnetic gradient switching. This is due to small induced currents within the semicircular canals of the inner ear leading to inappropriate stimulation of the mechanism of balance. A final disadvantage of high fields is that there are more susceptibility artifacts due to air–tissue interfaces. Open Systems While in some settings there is a growing trend toward the use of higher field strengths in enclosed circular magnets, in other settings, open MRI systems, which utilize lower field strengths (0.15–1.0 T) have become more widespread. These magnets are considered ‘‘open’’ because the magnets consist of two opposing magnetic plates with a space in between. Consequently, they do not require the patient to be enclosed by the bore of a magnet. Lack of image quality, previously a significant limitation of open systems, has been overcome to some degree by improved coil designs and better magnet designs. Open MRIs have a number of advantages over conventional enclosed units. Claustrophobic patients and children can be scanned in an open MRI without sedation. Some open MRI systems are configured so that the patient may stand during the scan thus loading the spine in a physiologic manner. This is primarily useful for vertebral disk disease and may find application in back pain related to potential metastatic disease and vertebral body instability. Open systems are generally preferred by patients. However, they also allow interventional procedures to be performed under MR guidance. For instance, stereotactic biopsies can be performed under real-time guidance, and this could permit more accurate needle guidance, minimizing risk to the patient while maximizing the diagnostic yield. Intraoperative open MRI systems can also be used to guide neurosurgery and other interventions (6–8). Open MRIs have recently improved in quality. However, due to their lower field strength they suffer from intrinsically lower SNR which results in longer scan times to preserve image quality. Furthermore, the lower field makes it difficult to

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perform studies such as spectroscopy and diffusion-weighted MRI, thus limiting the capabilities of the magnet. High Performance Gradients Many of the features most desired by clinicians such as resolution, slice thickness, and speed are determined not only by magnetic field strength but also by the strength and speed of the magnetic gradients. Magnetic gradients are small fields that are superimposed on the main magnetic field in order to change the resonance frequency of water, which in turn, allows the signal from the MRI to be localized and thereby generate images. Magnetic gradients are usually applied for very short periods during the scanning process. Stronger magnetic gradients permit thinner slices and higher matrices to be acquired thus improving the image resolution. The speed with which these strong gradients are applied, known as the slew rate, can improve the speed of scanning and also allow for extended spin and gradient echoes to be acquired, resulting in highly efficient scans in which images with varying T1 and T2 weighting can be obtained during a single acquisition. The sudden bursts of energy created by rapidly changing the local magnetic field of tissue induce small currents in the body. When strong gradients change quickly (‘‘high performance gradients’’), they may exceed the threshold of stimulation of peripheral nerves resulting in tingling or a ‘‘pins and needles’’ sensation in the arms and legs. Such peripheral nerve stimulation, while unpleasant is not dangerous. Much care is taken in the design of gradients to avoid peripheral nerve stimulation.

Parallel Coil Imaging A recent hardware/software innovation in acquisition MRI has been parallel coil imaging. Coils can be thought of as receiver antennas. In parallel imaging, instead of relying on one coil or even one set of many coils to collect a single data set, each coil in an array simultaneously collects a different part of the total image data (‘‘k-space’’); these parts are combined to form an image. As a result of this ‘‘parallel’’ processing, dramatic improvements in imaging speed can be achieved (9–11). For instance, if two coils are actively parallel, the scan time can be cut by a factor of two, and if four coils are active in the array, the scan time can, in theory, be cut by a factor of four and so forth. Parallel processing is possible only because of high-bandwidth receivers that allow the simultaneous acquisition of data from multiple coils and a custom-designed software that permits different coils to sample different parts of k-space. In this manner, head-spine arrays can be designed so that acquisitions can be greatly accelerated and are much more convenient for technologists since multiple coil changes are not necessary (11). Thus, a survey of the brain and spinal cord for metastatic disease can be performed in a single imaging session. Parallel imaging also permits higher temporal resolution in applications such as MRA, dynamic contrast enhancement, diffusion-weighted imaging (DWI), and spectroscopy. High bandwidth receivers also permit shorter pulse sequences. The primary disadvantage of parallel imaging is loss of signal and in applications where signal is already low (e.g., phosphorous spectroscopy), parallel imaging may not be feasible. Fortunately, this is of less concern in the brain where SNR is usually more than adequate.

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Moving Table The ability to control the speed and direction of table motion over wide areas of the body has enabled total body screening studies to be contemplated. Although not yet in wide use, total body imaging has been proposed for monitoring populations at high risk of developing primary or secondary tumors. Thus, head-to-toe scanning is technically feasible in less than one hour. Such scans are still considered experimental because risks and benefits to individuals as well as costs to society are yet to be rigorously analyzed. Largely due to considerations of time, it can be difficult to optimize the imaging parameters for each body part. Thus, the field of view used for chest imaging may also be used in the brain imaging, which lowers the effective resolution of brain imaging. Such scans are generally performed with fast sequences and compromises are made in resolution and contrast in order to achieve efficient coverage. The efficacy of total body screening has not yet been proven in large studies.

SOFTWARE ADVANCES Equally important as advances in hardware are advances in software or pulse sequence design. MR images are the final product of a carefully orchestrated sequence of radiofrequency pulses and magnetic gradients known as pulse sequences. Pulse sequences are designed to extract specific types of data about the brain. As a consequence they are often not optimized for other types of data. Thus, a complete MRI is composed of several complementary sequences from which a diagnosis can usually be made. Generic pulse sequences include those that are T1-weighted or T2-weighted. This designation simply means that although both T1 and T2 effects contribute to the image contrast, the image is dominated by either the T1 or T2 relaxation properties of the tissue. 3D SPOILED GRADIENT ECHO Stronger gradients have made possible ultrashort 3D imaging sequences that are T1-weighted. Scans that are less than 1 mm thick can be obtained through the entire brain. Because they depict the anatomy with exquisite detail they can be fused with images of lower resolution but have functional attributes such as positron emission tomography (PET) scans, which have lower spatial resolution but may be more sensitive for detecting pathology. High-resolution MR scans are termed ‘‘isotropic,’’ since they demonstrate the same resolution along any plane of section. Thus, a single isotropic 3D acquisition obtained in the coronal plane can be reformatted without loss of anatomic detail along any arbitrary line of section. These scans are used for surgical planning, intraoperative guidance, and radiotherapy planning. 3D images of brain tumors, after intravenous contrast, are obtained to accurately determine tumor volume during treatment.

Rapid Acquisition Relaxation-Enhanced and Fast Spin Echo A conventional T2-weighted spin echo sequence typically yields one echo for every 90 pulse which is then detected by a receiver coil. The rapid acquisition

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relaxation-enhanced (RARE) or fast spin echo (FSE) sequence shortens the time needed to acquire T2-weighted scans by obtaining multiple echoes for every 90 pulse. Whereas traditional T2-weighted scans require 6 to 10 minutes for scanning whole brain; T2-weighted RARE or FSE images are often acquired in two to five minutes. Although it was initially felt that such scans were not as sensitive to early pathology as peritumoral edema, in fact this has proven to be a minor disadvantage compared with the gain in speed and RARE and FES are now routine. The increased speed of acquisition can also be employed to generate higher resolution images and thus improve detection of small brain lesions. These sequences have the desirable property of reducing artifacts caused by susceptibility and thus are less influenced by surgical clips or air adjacent to the brain.

Echo Planar Imaging Echo planar imaging (EPI) is used to generate fast whole brain images within one to two seconds. EPI produces multiple echoes, similar to FSE but with many more echoes while the readout gradient is varying. This is known as an echo train, and it dramatically reduces scan time. To achieve such rapid scans, however, image resolution is generally compromised, and image quality is not as good as traditional sequences due to susceptibility artifacts. EPI is primarily used during 3D acquisitions for BOLD or diffusion imaging.

Gradient and Spin Echo Gradient and spin echo (GRASE) imaging is a recent innovation primarily used at high-field strengths to overcome some of the heating concerns that occur with T2-weighted scans at higher field strengths (12–14). By combining gradient echo and spin echo images in a single sequence, SAR can be reduced and T2-like contrast can be achieved within reasonable time constraints. Thus, sequences normally performed at 1.5 T can be modified to GRASE-type sequences at 3.0 T.

T2 Perfusion Imaging Susceptibility imaging for measurement of brain perfusion is performed by intravenously injecting a gadolinium chelate at a high rate (e.g., 5 cc/sec) and observing a decrease in signal related to the T2 effects of the rapidly administered gadolinium chelate within the brain parenchyma (15). This is accomplished by rapidly acquiring T2 EPI images and then analyzing these images on a pixel-by-pixel basis. The rapid drop in signal intensity followed by a rapid restoration of signal within the brain is related to the first pass of the gadolinium chelate bolus. The signal intensity-time curve is used to derive regional cerebral blood volume (rCBV), mean transit time (MTT), and regional cerebral blood flow (rCBF) (Figs. 1–3) (15–17). These are usually used in the context of strokes; however, their use in measuring brain tumor response to therapy is being explored (18). The breakdown of the blood brain barrier reduces the T2 effects by allowing dilution of the contrast bolus, thus leading to errors in rCBV and rCBF measurements, however, this property has been exploited to calculate vascular permeability within tumors.

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Figure 1 Transition of glioblastoma from low grade to high grade was first detected on T2 perfusion blood volume map. (A) Contrast-enhanced T1-weighted MRIs at three different dates demonstrates leakage of contrast media only on the third scan indicative of a high-grade tumor. (B) Blood volume maps, however, obtained with T2 perfusion demonstrate that abnormal blood volume could be detected earlier (arrow) than with the T1-weighted images.

Fluid-Attenuated Inversion Recovery Fluid-attenuated inversion recovery (FLAIR) is an MR sequence that suppresses CSF by applying an initial inverting 180 pulse, which negatively magnetizes the CSF and then waiting approximately for the T1 of CSF so that its magnetization (and hence signal) crosses a zero point just as the imaging data begins to be acquired (19). Thus, water signal is suppressed. It can be used in conjunction with long timeto-echo (T2-weighted) or short time-to-echo (T1-weighted) fast spin echo sequences. FLAIR suppresses signal from CSF and any other fluid with a comparable T1, thus it is nonspecific. FLAIR T2-weighted images improve the conspicuity of lesions, especially those located near CSF spaces, such as near the cerebral ventricles. Moreover, because fluid

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Figure 2 Value of blood volume maps in assessing brain tumors. Glioblastoma multiforme on T1-weighted (upper left), T1-weighted postcontrast (upper middle), and FLAIR (upper right) demonstrate, respectively, no abnormality, subtle enhancement, and edema in the region of the tumor indicative of a low grade tumor. The T2 perfusion blood volume map (lower left), however, demonstrates increased blood volume suggesting a higher-grade tumor.

is present along the cerebral convexities, FLAIR greatly improves the detectability of lesions located near the dura. Thus, FLAIR T2-weighting has become a new standard in imaging the brain (20,21). Contrast enhancement is a critical component of brain MRI, because it identifies sites of breakdown in the blood brain barrier which is often associated with

Figure 3 Comparison of MRI, PET, and blood volume maps. Left to right: FLAIR image, FDG PET, blood volume, and postcontrast T1-weighted image. Respectively these images depict: a lesion in the mass intermedia demonstrating high signal on FLAIR, relatively low metabolic activity on PET but increased blood volume despite absence of enhancement in the thalamus and insula suggesting a large, high-grade glioma.

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Figure 4 Advantage of FLAIR for detecting leptomeningeal metastases. Left to right: T1-weighted MRI before contrast, the T1-weighted MRI after intravenous contrast, and the FLAIR after contrast. Note that the enhancement of the subarachnoid space indicative of leptomeningeal involvement of the occipital lobe is seen only on the FLAIR.

tumor. Conventional T1-weighted imaging depicts enhancing lesions as foci of increased signal. For the brain parenchyma, T1-weighting alone is usually sufficient, however, it can be difficult to detect leptomeningeal invasion on conventional T1-weighted scans (Fig. 4). By using FLAIR sequences after contrast administration, it is easier to detect enhancement in the meninges or parenchyma since the CSF is suppressed in this sequence (Fig. 5) (19). Diffusion Weighted Imaging DWI is a technique that applies a series of increasingly strong magnetic gradients during acquisition. Water molecules that are restricted in their motion will not wander over these gradients during the acquisition and so will tend to retain signal; whereas tissues with highly diffusing water molecules will quickly lose signal with the

Figure 5 Advantage of FLAIR for detecting parenchymal lesions. Occasionally, FLAIR can depict parenchymal lesions better than other methods, although this is unusual. Left to right: T1-weighted MRI without contrast, T2-weighted MRI, T1-weighted MRI after intravenous contrast, and FLAIR after contrast. Note that two subtle lesions, one in the midbrain and one in the frontal lobe, are best seen on the FLAIR sequence.

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application of progressively stronger gradients as the signal is dissipated by the gradients (22). The loss of signal as a function of diffusion gradient strength can be quantified by the apparent diffusion coefficient (ADC) which becomes a convenient measure of diffusion. DWI has primarily been employed in evaluating early brain infarcts where the lower temperature within ischemic strokes decreases water diffusion, and therefore results in higher signal even before changes in tissue T1 and T2 can be detected (23–25). Therefore, DWI is used as a method of confirming the presence of a suspected brain infarct in its early hours. Its role in cancer imaging is more controversial. It is thought that intact tumors with their dense cellularity and intact cellular membranes have restricted diffusion due to these structural barriers. However, after treatment, as cell membranes become more disrupted and permeable, water is less restricted and is able to diffuse further, a process detectable as signal loss on DWI. This property of DWI makes it attractive as a method of demonstrating early responses to therapy (26). For instance, lymphoma with its tightly packed cells usually has restricted diffusion and is relatively bright on DWI compared to the detection of other tumors. Following treatment, however, the signal within lymphoma will drop on DWI indicating cellular membrane breakdown. Additionally, when attempting to differentiate between an abscess and a glioblastoma, DWI is often used because the water molecules within infected pocket of an abscess exhibit restricted diffusion (cellularity, mucus, proteinaceous debris, cytokines), and thus abscesses tend to be much higher in signal than do glioblastomas (27,28). However, ADC maps generated using the images obtained with different gradient field strengths can clarify the nature of the lesion (Fig. 6). Dynamic Contrast Enhanced-MRI DCE-MRI generally refers to a rapidly acquired T1-weighted volume acquisition before, during, and after the administration of a gadolinium chelate. This is distinct from the T2-weighted dynamic sequences described previously. These sequences require a rapid injection of contrast and ultrafast imaging, and are complete within a minute of injection after the first pass. T1-weighted, dynamic contrast-enhanced images are 3D volume acquisitions obtained every 2 to 10 seconds for a period of minutes and the kinetics reflect perfusion and permeability as well as vascular volume. The kinetics of the time–signal curve can be fit to a pharmacokinetic model from which parameters can be derived which relate to vessel permeability and relative vascular volume (Fig. 7). The theory of these models is covered in another chapter in this book. Since most MRI studies performed in oncology utilize gadolinium chelates, this functional test adds little total time to the imaging (29). Applications of DCE-MRI include attempting to distinguish radiation necrosis from recurrent disease. Tumors tend to have rapid enhancement and relatively rapid washout while necrosis enhances less intensely and more slowly, and therefore washes out more slowly. DCE-MRI has also been used to monitor therapies, particularly those directed against angiogenesis, because changes on DCE-MRI directly relate to changes in vascular permeability (30). MR Spectroscopic Imaging Brain tissue is complex and is composed of many metabolites, some of which have unique magnetic resonance frequencies. However, most conventional MRI scans

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Figure 6 Value of DWI and ADC maps. Left to right and top to bottom: T2, ADC, T1 without contrast, FLAIR, DWI, and T1 after intravenous contrast. Note that there is a ring-enhancing lesion in the right occipital lobe. The central portion is high in signal on DWI likely due to ‘‘T2 shine through,’’ since the T2-weighted scan show similar high signal. This alone could not differentiate a tumor from an abscess. The ADC map, however, demonstrates low signal indicating low diffusion in the center; higher in the periphery, indicative of pus-containing abscess.

Figure 7 Value of DCE-MRI for brain tumors. Left to right: fpv map, Ktrans map, and kep map as well as the standard T1-weighted contrast enhanced image. The fpv and Ktrans maps indicate that this is a highly vascular tumor with high vascular permeability. The histologic diagnosis was meningioma. (See color insert.)

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depend only on water and fat peaks to generate sufficient signal to generate an image. By selectively measuring the peaks of other metabolites relative to water, a spectrum can be generated that contains important clinical information. Two metabolites of particular importance in the brain are N-acetyl acetate (NAA) and choline (Ch) (31). NAA is a structural component of intact neural tissue. Choline is a membrane component of cells. In tumors, NAA would be expected to decrease in concentration whereas choline would be expected to increase in concentration. Thus, the ratio of NAA/Ch decreases in tumors compared to normal brain tissue, and this ratio appears to have prognostic information. Tumors with low NAA/Ch ratio have poorer prognoses. MR spectroscopic imaging (MRSI) can be used to detect tumors which are otherwise not visible. Many brain tumors particularly in the brainstem will not disturb the blood brain barrier and will not be detectable by T1-weighted, T2-weighted, or enhanced T1-weighted images (32). MRSI can be used to target biopsies and detect the true borders of tumors beyond the enhancing rim. It is also useful in distinguishing radiation necrosis and tumor recurrence (33,34). It has been used to direct radiation therapy of brain tumors. Recently, 3D multivoxel acquisitions have become possible so that spectra localized to specific brain regions can be obtained in a relatively time efficient manner. Color-encoded images reflecting precalculated metabolite ratios such as NAA/Ch simplify the interpretation of these complex data sets.

Blood Oxygen Level Determination Functional MRI (fMRI) refers to MRI of the brain performed while the subject is performing a specific task. For instance, a patient may be asked to repeatedly touch their thumb and forefinger together while being scanned, and this action causes a small change in blood flow to a specific motor center in the brain. The change in blood flow induced by this action causes a change in the ratio of deoxyhemoglobin to oxyhemoglobin. Deoxyhemoglobin is paramagnetic and relaxes relatively faster on T2-weighted images than does oxyhemoglobin. This small change in signal can be detected and is known as ‘‘blood oxygen level determination’’ scans as it depends on the deoxy/oxy hemoglobin ratio. The differential in T2 relaxation can be exploited to generate difference maps that are thought to reflect activity in the brain. It is important to note that this technique does not necessarily measure tissue oxygenation, i.e., the partial pressure of oxygen within tissue. Rather, it reflects the available oxygen carrying capacity within the hemoglobin of red blood cells within tissue; therefore, it does not necessarily reflect tissue hypoxia. BOLD imaging has been used to map motor, language, and sensory regions of the brain prior to neurosurgery in an effort to spare vital functions. It can be performed prior to surgery for removal of a tumor to modify the surgical approach, if the tumor is close to an important functional center. Instead of functionally challenging the brain, an extrinsic contrast agent can be given to augment hemoglobin saturation with oxygen. Making the patient to alternately breathe a mixture of carbon dioxide and enriched oxygen (5% CO2 and 95% O2), a mixture called carbogen, and room air can depict regions of hypoxia within tumors. Presumably, oxygen-deprived regions of a tumor will demonstrate larger changes in signal than do regions that are already satisfactorily served by an oxygenated blood supply. Thus, this technique may reflect tissue hypoxia.

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Magnetization Transfer MRI depends on a resonance phenomenon in which tissue at a specific magnetic field will resonate at a specific radiofrequency. However, some tissue resonates at slightly different frequency due to local influences on the magnetic field. This is known as ‘‘magnetization transfer (MT).’’ Off resonance, pulses will stimulate water protons in restricted spaces such as within cells, and these ‘‘bound’’ protons will then exchange with ‘‘free’’ water protons. If there is an exchange, it will serve to lower the signal from the target. This technique is especially useful in suppressing background tissue, and thus increases the conspicuity of white matter lesions. By suppressing background, there is also increased sensitivity to small enhancing lesions, however, because vessels are also high in signal on MT studies, it may be difficult to interpret such studies (35). IMAGE MANAGEMENT The increasingly sophisticated imaging capabilities of MRI necessitate the development of equally advanced image processing tools. Many of the techniques described above generate huge amounts of data. To be useful, these images must be synthesized into formats that are accessible to clinicians. Image management in MRI can be viewed as a series of events. After image acquisition, the images are sent to electronic workstations. Here they are stored in digital imaging and communications in medicine (DICOM) format, and then are processed using one or more different software tools. Since this is an increasingly time-consuming process, often a radiology department will form an image processing team comprising one or more individuals. Once the images are processed, they are sent back to the picture archiving and communications system (PACS) for storage and distribution. Current studies may generate between 1000 and 5000 images, and thus tax the storage capabilities of many conventional PACS systems. Fortunately, new hardware is beginning to address this critical issue.

CHALLENGES IN NEURORADIOLOGY While routine imaging of the brain for metastatic disease or for primary brain tumors is highly developed and considered generally to be quite successful, there remain a number of challenges. One of the most vexing issues is that concerning the nonenhancing brain lesion. As mentioned, these may require the acquisition of an MR spectroscopy in order to detect the lesion. However, such lesions raise perplexing problems. For instance, to what extent can one rely on imaging findings to dictate therapy, if the actual lesion can be detected only by a ratio of metabolites? Among lesions that are easily seen on MRI, there remain a number of significant challenges. For instance, having detected a small lesion how long should it be watched and when should surgery be performed? This topic is the subject of intense research and it is hoped answers will emerge in the near future. Therapy is influenced by tumor grade. Perfusion MR, DCE-MRI, and MR spectroscopy can be used to estimate grade based on a constellation of findings. MR results can be added to PET results to further refine the estimate of tumor grade. The coordination of this data and the synthesis of it into a coherent and reproducible algorithm are the subject of current research. Ultimately, bioinformatics, the

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management of quantitative biologic information combined with available clinical data may prove useful in helping to determine the nature of a lesion. Even with the numerous techniques available, radiation necrosis is still difficult to differentiate from progressive disease. Perfusion scans, MR spectroscopy, and DCE-MRI have been used in this setting, although none of these techniques is fully satisfactory. Again, multiparametric imaging may provide better answers in the future. CONCLUSIONS MRI of the brain is a vital part of modern oncology. In addition to very accurate T1-weighted, T2-weighted, and gadolinium-enhanced T1-weighted MRI, a number of other techniques including FLAIR, BOLD, DCE MRI, T2 perfusion imaging, MRSI, and DWI add diagnostic value. The increasing complexity of interpreting and teaching neuroradiology has led to increased specialization and the development of customized software tools. Hardware and image processing advances have combined to improve the diagnostic capabilities of brain MRI. Future developments include the discovery of molecular imaging probes with specificity for brain tumors, the ability to image tumors with intact blood brain barriers, and the development of early biomarkers of therapeutic success or failure that will aid in the treatment monitoring of patients undergoing therapy. REFERENCES 1. Frayne R, Goodyear BG, Dickhoff P, Lauzon ML, Sevick RJ. Magnetic resonance imaging at 3.0 Tesla: challenges and advantages in clinical neurological imaging. Invest Radiol 2003; 38:385–402. 2. Di Salle F, Esposito F, Elefante A, et al. High field functional MRI. Eur J Radiol 2003; 48:138–145. 3. Yacoub E, Duong TQ, Van de Moortele PF, et al. Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields. Magn Reson Med 2003; 49:655–664. 4. Nobauer-Huhmann IM, Ba-Ssalamah A, Mlynarik V, et al. Magnetic resonance imaging contrast enhancement of brain tumors at 3 tesla versus 1.5 tesla. Invest Radiol 2002; 37:114–119. 5. Di Costanzo A, Trojsi F, Tosetti M, et al. High-field proton MRS of human brain. Eur J Radiol 2003; 48:146–153. 6. McGirt MJ, Villavicencio AT, Bulsara KR, Friedman AH. MRI-guided stereotactic biopsy in the diagnosis of glioma: comparison of biopsy and surgical resection specimen. Surg Neurol 2003; 59:277–281. 7. Nabavi A, Gering DT, Kacher DF, et al. Surgical navigation in the open MRI. Acta Neurochir Suppl 2003; 85:121–125. 8. Nimsky C, Ganslandt O, Gralla J, Buchfelder M, Fahlbusch R. Intraoperative low-field magnetic resonance imaging in pediatric neurosurgery. Pediatr Neurosurg 2003; 38: 83–89. 9. de Zwart JA, Ledden PJ, van Gelderen P, Bodurka J, Chu R, Duyn JH. Signal-to-noise ratio and parallel imaging performance of a 16-channel receive-only brain coil array at 3.0 Tesla. Magn Reson Med 2004; 51:22–26. 10. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952–962. 11. European Federation of Neurological Societies Task Force. The future of magnetic resonance-based techniques in neurology. Eur J Neurol 2001; 8:17–25.

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12. Patel MR, Klufas RA. Gradient- and spin-echo MR imaging of the brain. Am J Neuroradiol 1999; 20:1381–1383. 13. Umek W, Ba-Ssalamah A, Prokesch R, Mallek R, Heimberger K, Hittmair K. Imaging of the brain using the fast-spin-echo and gradient-spin-echo techniques. Eur Radiol 1998; 8:409–415. 14. Hittmair K, Umek W, Schindler EG, Ba-Ssalamah A, Pretterklieber ML, Herold CJ. Fast flair imaging of the brain using the fast spin-echo and gradient spin-echo technique. Magn Reson Imaging 1997; 15:405–414. 15. Moseley ME, Wendland MF, Kucharczyk J. Magnetic resonance imaging of diffusion and perfusion. Top Magn Reson Imaging 1991; 3:50–67. 16. Kucharczyk J, Vexler ZS, Roberts TP, et al. Echo-planar perfusion-sensitive MR imaging of acute cerebral ischemia. Radiology 1993; 188:711–717. 17. de Crespigny AJ, Tsuura M, Moseley ME, Kucharczyk J. Perfusion and diffusion MR imaging of thromboembolic stroke. J Magn Reson Imaging 1993; 3:746–754. 18. Lee SJ, Kim JH, Kim YM, et al. Perfusion MR imaging in gliomas: comparison with histologic tumor grade. Korean J Radiol 2001; 2:1–7. 19. Herlihy AH, Hajnal JV, Curati WL, et al. Reduction of CSF and blood flow artifacts on FLAIR images of the brain with k-space reordered by inversion time at each slice position (KRISP). Am J Neuroradiol 2001; 22:896–904. 20. Castillo M, Mukherji SK. Clinical applications of FLAIR, HASTE, and magnetization transfer in neuroimaging. Semin Ultrasound CT MR 2000; 21:417–427. 21. Rumboldt Z, Marotti M. Magnetization transfer, HASTE, and FLAIR imaging. Magn Reson Imaging Clin N Am 2003; 11:471–492. 22. Bammer R. Basic principles of diffusion-weighted imaging. Eur J Radiol 2003; 45:169–184. 23. Li TQ, Chen ZG, Hindmarsh T. Diffusion-weighted MR imaging of acute cerebral ischemia. Acta Radiol 1998; 39:460–473. 24. Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology 2000; 217:331–345. 25. Roberts TP, Rowley HA. Diffusion weighted magnetic resonance imaging in stroke. Eur J Radiol 2003; 45:185–194. 26. van Rijswijk CS, Kunz P, Hogendoorn PC, Taminiau AH, Doornbos J, Bloem JL. Diffusion-weighted MRI in the characterization of soft-tissue tumors. J Magn Reson Imaging 2002; 15:302–307. 27. Nadal Desbarats L, Herlidou S, de Marco G et al. Differential MRI diagnosis between brain abscesses and necrotic or cystic brain tumors using the apparent diffusion coefficient and normalized diffusion-weighted images. Magn Reson Imaging 2003; 21:645–650. 28. Dorenbeck U, Butz B, Schlaier J, Bretschneider T, Schuierer G, Feuerbach S. Diffusionweighted echo-planar MRI of the brain with calculated ADCs: a useful tool in the differential diagnosis of tumor necrosis from abscess? J Neuroimaging 2003; 13:330–338. 29. Taylor JS, Reddick WE. Evolution from empirical dynamic contrast-enhanced magnetic resonance imaging to pharmacokinetic MRI. Adv Drug Deliv Rev 2000; 41:91–110. 30. Yang S, Law M, Zagzag D, et al. Dynamic contrast-enhanced perfusion MR imaging measurements of endothelial permeability: differentiation between atypical and typical meningiomas. Am J Neuroradiol 2003; 24:1554–1559. 31. Smith JK, Castillo M, Kwock L. MR spectroscopy of brain tumors. Magn Reson Imaging Clin N Am 2003; 11:415–429, v–vi. 32. Smith JK, Londono A, Castillo M, Kwock L. Proton magnetic resonance spectroscopy of brain-stem lesions. Neuroradiology 2002; 44:825–829. 33. Nelson SJ. Multivoxel magnetic resonance spectroscopy of brain tumors. Mol Cancer Ther 2003; 2:497–507. 34. Rand SD, Prost R, Li SJ. Proton MR spectroscopy of the brain. Neuroimaging Clin N Am 1999; 9:379–395. 35. Mathews VP, Caldemeyer KS, Ulmer JL, Nguyen H, Yuh WT. Effects of contrast dose, delayed imaging, and magnetization transfer saturation on gadolinium-enhanced MR imaging of brain lesions. J Magn Reson Imaging 1997; 7:14–22.

3 Advances in Ultrasound David Cosgrove, Chris Harvey, Martin Blomley, and Rob Eckersley Imaging Sciences Department, Imperial College, Hammersmith Hospital, London, U.K.

A Note from the Editors

U

ltrasound remains the key imaging modality for the routine assessment of patients with cancers. The last few years have seen notable developments on the ease of use of these machines, but have also heralded the introduction of new techniques that will expand the roles played by ultrasound. These arise from developments in contrast media that are based on gas filled microbubbles (now available in Europe and Japan), the development of elasticity imaging, and the use of ultrasound for therapy. The latter includes the use of ultrasound as a means of inducting heat-mediated tissue coagulation and as a way to target the tumor circulation.

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Part I. Advances in Mature Imaging Technologies

INTRODUCTION Ultrasound (US) has undergone impressive technical developments since the earliest static scanners with the progressive introduction of gray scale (1970), real time (1975), multi-element arrays (1975), pulsed Doppler (1970), color Doppler (1985), and the conversion to digital systems (1985); all of which have extended its reliability and ease of use so that now almost 25% of all clinical imaging studies worldwide are US examinations (1). The last five years have seen an emphasis on ease of use, with control panel ergonomics greatly improved and the development of systems to improve imaging such as automatic time gain compensation (TGC) and Doppler settings, which not only speed examinations but also improve reproducibility. Radical innovations of particular importance in oncology are the development of safe and effective contrast agents for US in the form of microbubbles, the development of elasticity imaging (elastography), and the use of US in therapy, both as a means of heat-coagulating tissue (high intensity focused ultrasound, HIFU) and as a way to improve drug delivery, either on its own or in combination with microbubbles.

IMAGING AND DOPPLER Both gray scale and Doppler imaging have been greatly improved by the combination of new transducer technologies and digital signal processing.

Transducers Transducers remain crucial to obtaining high quality images and Doppler, and have been the focus of extensive development. The piezoelectric material used is paramount in the performance of the transducer. Lead zirconate titanate (PZT) has been used for decades but it has a much higher acoustic impedance than that of tissue. This impedance mismatch results in strong reflections at the skin–probe interface; these decrease sensitivity and increase the ring down time of the transducer, which lengthens the transmitted pulses thus reducing spatial resolution. Multiple reflections between the surfaces of the transducer can also be transmitted into the tissue to produce reverberation artifacts. One solution is to combine PZT with a material of low acoustic impedance, such as epoxy resin, to form a composite transducer. Channels are cut into the PZT and filled with resin, thus reducing the acoustic impedance mismatch with tissue and simultaneously preventing waves traveling along the PZT, which otherwise confuse the clean shape of the transmitted beam. New piezoelectric materials, such as ferroelectric relaxors, are currently under evaluation (2). They are much more efficient in their conversion of electrical to sound energy, with acoustic impedance better matched to tissue than PZT. US transducers have a limited range of frequencies over which they are effective, their ‘‘bandwidth.’’ A large bandwidth improves the axial spatial resolution, and offers the possibility of using two or more frequencies in a single probe with cost and ergonomic savings. It is also essential for harmonic imaging. Bandwidth has been increased by applying multiple matching layers to the front of the transducer; these optimize electromechanical coupling so that the transduction of electrical energy to sound energy (and vice versa) is more efficient. Analogous matching layers on camera lenses lend them iridescent blue or yellow tints.

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Transducer technology has also addressed the problem of lack of control of the beam’s shape in the Z axis (orthogonal plane). Conventionally, the slice thickness is reduced by fixed focusing using a lens or by curving the transducer. However, if the transducer material is sliced across as well as along the block and appropriate electrodes applied, the inner elements across the array can be fired with a slight delay relative to the outer ones and the beam is electronically focused in the Z plane exactly as is normally performed in the X plane. This adds flexibility to the orthogonal focus so that the beam thickness can be controlled for different depths. These so-called ‘‘matrix arrays’’ are now becoming widely available despite the manufacturing challenge they pose and the added cabling and electronic complexity. An elegant way of achieving a similar effect without these overheads is the plano-concave transducer known as the Hanafy lens, after its inventor. The front of the transducer is made concave so that its edges are thicker than the central strip. Thus, the edges respond to lower frequencies and the center to higher; since lower frequencies penetrate to greater depths, the focus in the Z plane is progressively and automatically optimized.

Signal Processing Harnessing the power of fast digital computers to US has resulted in a number of important improvements in imaging and in convenience and reproducibility (3). Harmonics The discovery that sound propagates through the tissue in a nonlinear fashion to produce overtones of the transmitted signal (harmonics) has improved contrast resolution and reduced artifacts that result from beam imperfections (4). The principle is simple: tissue is slightly compressed during the positive pressure phase of the ultrasonic wave (and vice versa); sound travels faster in a denser medium and so the compression part of the cycle travels faster so that the wave looses its original symmetry. Such an asymmetric wave contains harmonics, which can be selected from the returning echoes either by applying a frequency filter or by comparing the echoes from a pair of pulses transmitted with opposite phase; the linear signals cancel, leaving the harmonics for image formation. Because these tissue harmonics are more strongly elicited by higher power US, artifacts caused by the side lobes and reverberations are relatively reduced, resulting in a cleaner, less noisy image (Fig. 1). In effect, the imaging beam is generated from within the tissue itself. Compounding Compounding is a technique in which two or more images acquired simultaneously from the same tissue are superimposed; this reduces the speckle content, which has a random distribution and gives a smoother, more anatomically correct image (Fig. 2) (5). An obvious way is to collect two images from different angles, a method that was widely used with the original articulated arm static scanners. Using the electronic beam steering of an array transducer, overlapping scans (varying from three to nine frames) from different angles can be acquired in real time with only a modest loss of frame rate. Alternatively, the same effect can be achieved by collecting data at different frequencies and the two methods can be combined. The scans are then averaged to produce real-time compound images.

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Figure 1 Tissue harmonics. The echo-poor region in segment 4 (arrowheads in A) has the appearances of focal fatty sparing but its outlines are more clearly seen when imaged with tissue harmonics (B) using the same settings. Tissue harmonics improves contrast by reducing side lobe and reverberation artifacts. The patient was being staged for breast cancer; the typical benign appearances made further investigation unnecessary.

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Figure 2 Compound scan. Compared with the conventional image of this thyroid cancer (A), the compounded image (B) shows more detail because the information from beams at three different angles has been combined, and also there is more contrast because the speckle noise has been reduced.

Display Extended Field of View (EFOV) When US was introduced into clinical practice in the 1970s, the transducer was mounted on an articulated arm and swept to produce extended field of view images but this was sacrificed when real-time US was developed. Advances in computing

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Part I. Advances in Mature Imaging Technologies

Figure 3 Extended field-of-view. In this patient with extensive polycystic liver disease, the liver was too large to fit on a single scan. However, using the extended field of view technique (Sciescape, Siemens, Germany) the entire liver could be measured.

that track the speckle pattern of the images as the transducer is swept across extended anatomical regions now allow high-resolution EFOV images to be created in gray-scale or color Doppler (Fig. 3). Large lesions can be included in a single image and also provides a better display of their anatomical relationships. It is an excellent teaching aid; it allows measurements of large structures to be performed and serves as a useful record for follow-up. 3D Imaging Until comparatively recently US has lagged behind both CT and MR in threedimensional (3D) imaging. The 3D US is based on reconstruction algorithms and is therefore dependent on high quality 2D data, which has been limited by speckle, side lobes, clutter, and other artifacts. Recent improvements in imaging (harmonic imaging, nonlinear signal processing, and 2D matrix array transducers) have reduced these problems and paved the way for useful 3D imaging, while fast computing has allowed the production of real-time 3D scans (so-called 4D US imaging) (VolusonÕ 730, GE-Kretz). A position sensor attached to the transducer or an arrangement whereby the transducer is swept across the volume by a motor provides the necessary positional data. Volumes can be displayed as either series of multiplanar reformats or rendered 3D images, which improve appreciation of the relative position of structures, including flowing blood. Currently, the main clinical application is in obstetrics but the approach shows promise in breast and prostate cancer, and reveals the complexity of tumor vascularity in an elegant way (Fig. 4) (6). In interventional procedures, 4D US is promising for needle biopsy guidance while 3D US is used to guide radioactive seed implants in the prostate and for breast

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Figure 4 Three-dimensional scan with Doppler. In this three-dimensional reconstruction of a breast mass (arrows), the image has been duplicated and opposite halves of the gray scale portion removed from the opposing sides to reveal the arrangement of the blood vessels using power Doppler (shown in red). Corresponding portions of the vasculature are numbered. This form of display allows the vessels to be seen in their relationship to the mass. Source: Photos courtesy of Drs. W.E. Svensson, S. Kyriazi, and K. Humphries. (See color insert.)

biopsy and a stereotactic system has been developed for needle delivery using electromagnetic position sensors (7). Coded Excitation One of the limits to the sensitivity of US imaging is the amount of acoustic energy that can be deposited in the tissues. Rather than increasing the transmit power, one way to get around this limitation is to use longer pulses. Generally, longer pulses mean poorer spatial resolution along the beam but this can be overcome if the acoustic pulses are digitally encoded: the received signals are then processed to

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Part I. Advances in Mature Imaging Technologies

recover the spatial information. Examples of coding strategies include varying the frequency (e.g., as a chirp of rising frequency) or amplitude (complex shapes that are similar to those used in mobile phone technology to allow a band to carry multiple signals and retain their integrity). Using this approach the sensitivity of the US scanner to weaker echoes can be improved. Therefore, higher frequencies can be used and still separate it from noise to give better spatial resolution down to greater depths. Automatic Optimization Systems Automatic Gain Compensation Modes set the correct gain in 2D at any point across the image with a single switch. This is achieved by the processor analyzing the distribution of gray levels in each part of the image and adjusting the level of each pixel to optimize local contrast. This not only improves image quality rapidly but also offers better consistency between operators, thus reducing operator dependence. Automatic optimization systems are also available for color and spectral Doppler where they unwrap aliasing by shifting the baseline and optimizing the pulse repetition frequency (PRF). They can also automatically place the angle correction cursor by ‘‘looking at’’ the walls of the vessel in which the sample volume has been placed and noting their position. Dhotopic Ultrasound Imaging (Elegra; Siemens, Germany) is a real-time post processing technology, which takes advantage of the eye’s perception of light to optimize gray-scale tissue differentiation. X-res (Philips-ATL, U.S.A.) is a postprocessing system that seeks to display the image brightness in an optimal fashion; it derives from MRI display processing and reduces speckles. Contrast Agents US, unlike all other imaging modalities, has lacked effective contrast agents until comparatively recently. This was rectified with the introduction of microbubbles in the 1990s; they have revolutionized clinical and research applications in this field and provided the stimulus for the development of harmonic modes to improve conventional imaging as well as microbubble-specific modes that have proved useful for staging and for characterizing tumors among other applications (8,9). They also provide opportunities for unique functional studies (10,11). Their small size and short life in the blood as well as their inert composition has meant that they are very safe with no significant adverse effects; in particular, they are not nephrotoxic and do not cause cardiovascular changes. A large number of agents have been developed and several have been introduced into clinical practice (Table 1). Principles The microbubbles used as contrast agents for US are made to be smaller than 7 mm in diameter so that they can cross capillary beds. When administered intravenously, these agents flood the blood pool and (usually) remain within the vascular compartment. They must survive passage through the cardiopulmonary circulation to produce useful systemic enhancement. An ingenious range of methods has been deployed to achieve the required stability and to provide a clinically useful duration of enhancement. Both the gas contain usually air or a perfluoro gas and the stabilizing shell (denatured albumin, phospholipids, surfactants, or cyanoacrylate) are critical in this respect. The first agent for cardiac use, Albunex, had an albumen shell and contained air. The first generally used agent, Levovist, consists of galactose microcrystals whose surfaces provide nidation sites on which air bubbles form when they are

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Table 1 Classification of Ultrasound Microbubbles Microbubble Air-based agents Agitated saline Albunexa Echovistb Levovistb SHU 508A Quantisona

Gas

Stabilization

Company

Air Air Air Air Air

None Sonicated albumin None Palmitic acid Dried albumin

N/A Tyco Schering Schering Andaris Ltd.

Perfluoro agents BR14 Echogena QW3600 Definity perflutren Imagent AFO150 Optison Perfluorocarbon Exposed Sonicated Dextrose Albumin PESDA Quanfuxian QFX

Perfluorobutane Dodecafluoropentane Perfluoropropane Perfluorohexane Perfluoropropane Perfluorobutane

Phospholipids Emulsion, surfactants Phospholipids Surfactants Albumen Sonicated albumin

Bracco Sonus Bristol-Myers Squibb Schering Amersham Health University of Nebraska

Perfluoro

Sonicated albumin

SonoVue BR1

Sulfur hexafluoride

Phospholipids

Nanfang Hospital, Guangzhou, China Bracco

Liver trophic agents Levovist SHU 508A Air Sonavista SHU 563A Air Sonazoida NC100100 Perfluorocarbon a

Palmitic acid Cyanoacrylate Not published

Schering Schering Amersham (Nycomed)

No longer under development or marketed. Licensed for clinical use.

b

suspended in water; the resulting microbubbles are stabilized by a trace of a surfactant, palmitic acid. An improved version of Albunex, Optison, is filled with perfluoropropane while a family of perfluoro gas containing agents such as SonoVue and Definity use phospholipids as the membrane are becoming important in clinical practice. Interactions of Microbubbles with US Waves The interactions of microbubbles with an US beam are complex (12–14). Their gas content makes them much more compressible than soft tissue and so, when exposed to the compression–rarefaction wave of an ultrasonic pulse, they undergo alternate contractions and expansions. Like all oscillating systems, they vibrate most readily at a particular frequency, their resonance frequency. For microbubbles